Contrast based imaging and analysis computer-implemented method to analyze pulse thermography data for nondestructive evaluation

ABSTRACT

Methods and systems for analyzing and processing digital data comprising a plurality of infra-red (IR) video images acquired by a pulse thermography system are used to compute video data from the raw and smoothed video data acquired for the performance of non-destructive evaluation. New video data types computed may include but are not limited to contrast evolution data such as normalized contrast, converted contrast and normalized temperature contrast. Additionally, video data types computed comprise surface temperature, surface temperature rise and temperature simple contrast.

CROSS-REFERENCE TO RELATED PATENT APPLICATION(S)

This patent application claims the benefit of and priority to U.S.Provisional Patent Application Ser. No. 62/376,276, filed Aug. 17, 2016,the contents of which are hereby incorporated by reference in theirentirety.

STATEMENT OF GOVERNMENT INTEREST

The invention described herein was made by employee(s) of the UnitedStates Government and may be manufactured and used by or for theGovernment of the United States of America for governmental purposeswithout the payment of any royalties thereon or therefor.

FIELD OF THE INVENTION

The invention is directed to the general fields of nondestructiveevaluation (NDE) and of processing of data acquired from an infraredcamera. Specifically, the systems and methods described herein concernthe performance of a series of steps for analyzing and processingdigital data comprising a plurality of infra-red (IR) video imagesacquired by a system used for non-destructive evaluation.

BRIEF OVERVIEW

Infrared (IR) flash (or pulsed) thermography is an example of atechnique for non-destructive evaluation (NDE) used primarily in theinspection of thin nonmetallic materials such as laminated or bondedcomposites in the aerospace industry. It is primarily used to detectdelamination-like anomalies, although surface cracks are also detectedto some extent. In most circumstances, a single sided or reflectiontechnique is used where the flash lamp (heat source) and the IR camera(detector) are on the same side of the test object inspected.

The hardware equipment for an IR flash thermography system comprises aflash lamp (source of light/heat), a flash hood, a flash powersupply/trigger unit, a flash duration controller, an IR camera forcapturing video images, data acquisition electronics, and a personalcomputer (PC). See FIG. 1 for a schematic of a typical flash-hood setup. The PC is used for controlling the flash trigger, for acquiringvideo data from the IR camera, for displaying data, and forpost-processing of the acquired data.

As one example of an NDE technique using IR flash thermography, a plateis provided as a test object with a round delamination in the center.See FIG. 2 for schematic of cross section of test object. In oneexample, the test plates have flat bottom holes drilled in them tosimulate controlled diameter and depth delaminations as shown in FIG. 3.The holes may or may not be drilled to the same depth. In FIG. 4, theholes are drilled to different depths. There are five sizes of thediameter and three holes for each size. Thus, there are five groups ofthree identical holes. Hole sizes and their depths are for specimen B ofFIG. 3 that are given in FIG. 4. Holes in each row have constant depthand remaining wall thickness.

After applying heat to the top surface of the test object by triggeringthe flash lamp, the top surface area surrounding the anomaly coolsfaster than the top surface (footprint) area above the anomaly. The IRcamera captures a sequence of images of the surface temperature in termsof pixel intensity and shows the anomaly as a hot spot (e.g., an areawarmer than the surrounding area or the reference region ofinterest—ROI) that is about the size and shape of the anomaly footprint.Relative pixel intensity, i.e., the difference in pixel intensity at thehot spot (measurement ROI) and the surrounding area (reference ROI),varies with the post-flash time. Example locations for measurement andreference ROI are illustrated in an example IR image of a graphitephenolic calibration standard shown in FIG. 5.

Deeper anomalies appear at later times in the IR video data compared tothe near surface anomalies. After the appearance of an anomaly in the IRvideo data, the relative pixel intensity continues to increase withtime. The relative pixel intensity of the anomaly reaches a peak at acertain time and then the relative pixel intensity decays until thetemperature of the indication area and the temperature of thesurrounding area become equal.

As discussed hereinafter, the embodiments described herein (a) definethe normalized image contrast and the normalized temperature contrastand use calibrated simulation to interpret the contrast evolutions, and(b) calculate and image contrast video sequence data and contrastfeatures. The embodiments described herein develop relationships betweenthe normalized image contrast and the normalized temperature contrast byusing equations of heat transfer. The embodiments described herein alsodevelop methods of contrast video sequence data processing and featureimaging for anomaly detection and characterization as an enhancement ofthe contrast method.

SUMMARY

The embodiments described herein are applicable to flash (or pulsed)infrared thermography nondestructive evaluation and is an advancement ofthe various methods and systems described in U.S. Pat. Nos. 9,066,028and 8,577,120. Contrast methods that further embody these methods andsystems include: three contrast video image processing methods forprocessing raw or smoothed video imagery data in terms of pixelintensity into corresponding video thermal response data forms and theirderivative video image data forms; a calibration and depth evaluationmethod; use of threshold in data analysis and other analysis methods;and thermography response-based probability of detection analysis.

The embodiments described herein may be divided into three categories,e.g., normalized or calibrated contrast evolution analysis, contrastfeature imaging analysis, and thermography response-based accept/rejectand probability of detection (POD) analysis.

Under the contrast evolution analysis, there are described herein threeContrast Video Image Processing (CVIP) methods as given below.

One contrast video image processing method is described herein as theNormalized Contrast and Derivatives (NCD) method. See generally FIG. 6.A second CVIP method is described herein as the Converted Contrast andDerivatives (CCD) method. See generally FIG. 7. A third CVIP method isdescribed herein as the Normalized Temperature Contrast and Derivatives(TCD) method. See generally FIG. 8. The three methods described hereinmay be mutually exclusive, meaning each may be processed independent ofthe others. In other words, FIG. 6, FIG. 7 and FIG. 8 show anindependent processing path for each method. Depending upon the userrequirements, each method provides specific benefits.

Normalized Contrast and Derivative (NCD) Method

As shown generally in FIG. 6, normalized contrast video processing usesa change in pixel intensity from a discrete set of data (frame)sequences for further video data computing. Two methods of determiningderivatives with respect to frame number (or time) have been provided,with each of the two methods branching into two possibilities asdiscussed below.

Method 1A: Derivative of raw or smooth and filtered (processed) contrastevolution.

Method 1B: Derivative of raw or smooth and filtered contrast evolutionwith smoothing during derivative computation.

Method 2A: Derivative of the simulation fit to a raw or filteredcontrast evolution. The simulation fit is also a curve fit (e.g.polynomial fit).

Method 2B: Derivative of curve fit (non-simulation) to the raw or smooth(or processed) contrast evolutions.

In each method, the contrast video sequence data is computed and thenconverted to first derivative video sequence data and second derivativevideo sequence data. Derivatives are computed from filtered and smoothed(or processed) data or fitted data only. Derivatives are not typicallycomputed on raw (unfiltered) contrast evolutions and some level ofsmoothing is applied to both first and second derivative. These videosare examined by playing the video forward or backward and selectingappropriate video frames for further analysis. This method is known asFrame Image Analysis (FIA).

Video Frame Images (e.g., normalized contrast at frame number 10) can bedisplayed for contrast and its derivatives and analyzed. Extractedfeature images are generated by extracting features from a selectedblock of processed videos and then having them analyzed. This method isknown as Extracted Image Analysis (EIA). Several non-derivative (e.g.peak or peak contrast, frame number at peak contrast) and derivative(e.g. peak first and second derivatives) features are extracted to makethe images. Depending upon the extracted feature images, the images mayreveal anomaly information such as the anomaly depth (frame number andderivative related images), anomaly size (contrast and contrastderivatives), and anomaly gap thickness (peak product time) and providesuppression of temporal and spatial noise. The images are described asthe contrast A-scan (i.e. normalized contrast evolution at a pixel),contrast B-scan (i.e. stack of contrast evolutions for pixels on aline), contrast value C-scan (i.e. peak normalized contrast image) andcontrast time of flight (TOF) C-scan (e.g. frame number at peak contrastimage) similar to traditional industrial ultrasonic testing (ASTM F2375) pulse/echo scans.

Converted Contrast and Derivative Method (CCD)

As shown generally in FIG. 7, converted contrast video processing uses achange in pixel intensity after a flash of heat and a multiplier for ameasurement and a reference region of interest (ROI). The method allowsextraction of pixel intensity-based converted contrast evolutions thathave similar shape characteristics to corresponding normalized contrastevolutions. Pixel intensity contrast evolution data is transformed inaccordance with this method. The converted contrast evolutions could beanalyzed with measurement of converted contrast peak amplitude andconverted contrast peak time. In addition, the first and secondderivatives can also be computed. A method is provided to computenormalized contrast from the converted contrast evolutions. Thederivative computation, selected Frame Image Analysis (FIA) andExtracted Image Analysis (EIA) are also implemented under the ConvertedContrast Method. Similar to the description related to the computing ofnormalized contrast video, A-scan, B-scan and C-scans are possible withthe converted contrast and the imaging results being comparable orbetter than the normalized contrast A-scan, B-scan and C-scans due tolower noise in the data.

Normalized Temperature Contrast and Derivatives (TCD) Method

As shown generally in FIG. 8, Normalized Temperature Contrast videoprocessing uses a sequence of frames of surface temperature in terms ofpixel intensity and computes video data using a change in pixeltemperature. Temperature rise and simple contrast video sequence dataare computed in this method. First and second derivative video sequencedata of smooth normalized temperature contrast data are computed.Selected Frame Image Analysis (FIA) and Extracted Image Analysis (EIA)are also applicable to Normalized Temperature Contrast method. Similarto that for the normalized contrast video, A-scan, B-scan and C-scansare possible with the temperature contrast and imaging results arecomparable to the normalized contrast A-scan, B-scan and C-scans.

The temperature contrast method reduces influence of diffused reflectionfrom the part surface, enhancing the contrast. Also, surface temperaturemeasurements are more quantitative than the pixel intensity measurementswhich contain both the emissive and reflective components of irradianceforming the image.

Contrast Evolution Calibration and Analysis (CECA) Method

In another embodiment, an empirical method of calibrating the flashthermography response in nondestructive evaluation is described. Thecontrast evolution calibration and analysis method (CECA) as showngenerally in FIG. 9 is applied to contrast evolution data for a pixelwith peak of relative contrast for the indication.

First, a physical calibration standard with artificial flaws such asflat bottom holes with desired diameter and depth values in a desiredmaterial is fabricated. An example calibration standard is shown in FIG.4. Long flat bottom slots can be used in the calibration standard. Fortight delaminations, use a standard that simulates the desiredcondition. U.S. Pat. No. 8,577,120 provided comparisons of normalizedcontrast response from slots and holes. As described in the '120 patent,slot width is mapped to equivalent flat bottom hole width, which thencan be used in evaluating depth of long indications.

In this embodiment, normalized contrast evolution data for eachartificial flaw in the reference standard is extracted from the rawvideo sequence data to provide calibration files that are loaded as abatch of calibration files as shown in FIG. 9. Contrast evolution filesare analyzed in the contrast evolution evaluation methods provided. Sixcontrast parameters are preferably extracted for each flaw in thecalibration standard. A calibration data set is prepared from thecontrast parameter data. The calibration data is plotted by using anevolution evaluation and calibration method described herein. In orderto analyze a given contrast evolution for flaw depth, contrast evolutionparameters are calculated, and diameter or widths are measured in 2Dimages of the anomaly. Depth is assessed in this method by using theanomaly diameter or width, the six contrast evolution parameters, andcalibration data. A single depth estimate can be interpolated from themultiple depth estimates, preferably one each from the six evolutionparameters.

Other Image and Data Analysis Methods

These methods include providing Frame Images, Extracted Images, andAnalysis of saved images. Video frame images are called Frame Imageshere. A frame number is associated with the frame image. Imagesextracted by scanning for values from multiple frames are calledExtracted Images. A single frame number is not associated with anextracted image.

Within the three data analysis methods under this category, there areother sub-methods, including flaw size measurement, edge detection,image gray value profiling along vertical or horizontal lines, and imagegray value profiling using peak of values scanned in vertical andhorizontal directions.

FIG. 10 shows process flow chart for Extracted Images. Various examplesof Extracted Image choices are discussed herein that provide usefulinformation depending upon user need.

A method of image comparison (registration, subtraction andsuperimposition) to assess changes in thermography response (i.e. raw orprocessed pixel intensity data) and image tiling or mosaic is also used.Saved images are further analyzed by creating a mosaic. The images canbe compared to reference images by a process called image registrationand then subtracted from the reference images. Thus, differences in thethermal response (e.g., normalized contrast) can be quantified.

Thermography Response, Accept/Reject Threshold and Pod Analysis

The methods described under this category include quantitativethermography response such as the peak normalized contrast, peakconverted contrast and peak normalized temperature contrast, simplecontrast and referenced simple contrast. These methods can be used forflaw detection based on establishing an accept/reject threshold levelfor a thermography-based response. These thermography-based responsescan then be used in probability of detection (POD) analysis using athermography-based response correlation to diameter/depth ratio or acorrelation of thermography-based response to both diameter and depthgiven as fitted surfaces.

BRIEF DESCRIPTION OF DRAWINGS

A more complete understanding of the embodiments described herein andmany of the attendant advantages thereto will be readily appreciated asthe same becomes better understood by reference to the followingdetailed description when considered in conjunction with theaccompanying drawings, wherein:

FIG. 1 provides a schematic of an exemplary single-sided flashthermography system.

FIG. 2 provides a cross sectional view of a test object, a referenceregion of interest (ROI), and a measurement ROI.

FIG. 3 provides pictures of reinforced carbon-carbon specimen A andgraphite-phenolic calibration standard B.

FIG. 4 provides a schematic of a back side of the graphite-phenoliccalibration standard B from FIG. 3.

FIG. 5 provides an IR image of the graphite-phenolic calibrationstandard B of FIG. 4 from raw data.

FIG. 6 shows a flowchart of Normalized Contrast Video Processing inaccord with one embodiment described herein.

FIG. 7 shows a flowchart of Converted Contrast Video Processing inaccord with one embodiment described herein.

FIG. 8 provides various sub-methods in Anomaly Depth Analysis method inaccord with one embodiment described herein.

FIG. 9 provides various sub-methods in Anomaly Depth Analysis method inaccord with one embodiment described herein.

FIG. 10 shows Extracted Images Processing Steps after computing any ofthe three forms of Processed Data from FIG. 6-9 in accord with oneembodiment described herein.

FIG. 11 shows a flowchart of various Probability of Detection Analysismethods in accord with one embodiment described herein.

FIG. 12 provides an example of a contrast evolution data file updatedwith calibration parameters in accord with one embodiment describedherein.

FIG. 13 shows examples of raw Normalized Contrast and smooth (orprocessed) derivative evolutions for hole number 4 of Specimen A withtwo values of smoothing points for comparison in accord with oneembodiment described herein.

FIG. 14 shows Extracted Images of peak Normalized Contrast (Cmax), peakfirst derivative of the Normalized Contrast (C′max), peak and secondderivative of Normalized Contrast (C″max) in accord with one embodiment.

FIG. 15 shows Normalized Contrast evolutions as B-scan along a verticalline through holes with numbers 4 to 9 of specimen A in accord with oneembodiment.

FIG. 16 shows an example of curve fit to Normalized Contrast evolution,including curve fit equation, fit data and a plot of curve fit equationfor each frame number in accord with one embodiment.

FIG. 17 shows an example of fitted Normalized Contrast evolution and itsderivatives in accord with one embodiment.

FIG. 18 shows an example of Normalized Contrast and Converted Contrastevolutions and their derivatives in accord with one embodiment.

FIG. 19 shows an example of low-pass filtering of Converted Contrastevolution and its derivatives in accord with one embodiment.

FIG. 20 shows Frame Images for Converted Contrast and Converted Contrastderivatives in accord with one embodiment.

FIG. 21 shows an example of Converted Contrast Evolution at ReferenceRegion of Interest (ROI) in accord with one embodiment.

FIG. 22 shows an example of comparison of Normalized Contrast evolutionwith Converted Contrast evolution at same pixel location in accord withone embodiment.

FIG. 23 shows an example of reference region of interest (ROI)evolution, line fit, slope and multiplier in accord with one embodiment.

FIG. 24 shows Extracted Converted Contrast images of peak contrast,standard deviation of peak contrast, peak of contrast first derivative,and frame number at peak of contrast second derivative (time of flightscan) in accord with one embodiment.

FIG. 25 shows an input display of a computer-implemented TemperatureMeasurement method set-up for a reinforced carbon-carbon test partlabelled as Specimen C in accord with one embodiment.

FIG. 26 shows an example of Normalized Temperature Contrast evolutionand its derivative evolutions at a selected measurement pixel in accordwith one embodiment.

FIG. 27 shows Normalized Temperature Contrast Extracted Images of peakcontrast, standard deviation of peak contrast, and peak of contrastfirst derivative, and peak of contrast second derivative in accord withone embodiment.

FIG. 28 shows a method of matching Fit (Simulation) Curve to NormalizedContrast Evolution in accord with one embodiment. As shown, the CurveFit is not yet matched.

FIG. 29 shows a Chi-Square Probability Density Function for variousvalues of k in accord with one embodiment.

FIG. 30 shows a method of matching Fit Curve to Normalized ContrastEvolution in accord with one embodiment. As shown, the Fit Curve ismatched.

FIG. 31 shows an example of computation of difference between Curve Fitand Normalized Contrast evolution in accord with one embodiment.

FIG. 32 shows an example of Calibration Data in accord with oneembodiment.

FIG. 33 shows Calibration Data surfaces for the six Normalized ContrastEvolution Parameters in accord with one embodiment.

FIG. 34 shows a main panel for the Contrast method and showsorganization of sub-methods of Smoothing and Filtering, Evolutions,Contrast Computation and Analysis in accord with one embodiment.

FIG. 35 shows a distance or flaw size measurement method in accord withone embodiment.

FIG. 36 shows surface fitted to peak Normalized Contrast data, chosenpeak contrast plane, intersection curve defining relationship betweenflaw size and depth in accord with one embodiment.

FIG. 37 shows a surface fit to peak contrast data with 90% predictionbounds in accord with one embodiment.

The above general description and the following detailed description aremerely illustrative of the exemplary embodiments, and additional modes,advantages, and particulars of the exemplary embodiments will be readilyapparent to those skilled in the art, now having the benefit of thisdisclosure, without departing from its spirit and scope as set forth inthe appended claims.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Detailed descriptions of the exemplary embodiments are provided herein.It is to be understood, however, that these embodiments may be modifiedin various forms. Therefore, specific details disclosed herein are notto be interpreted as limiting, but rather as a basis for understandingthe claims and as a representative basis for teaching one skilled in theart how to employ the exemplary embodiments described herein forvirtually any appropriately detailed system, structure or manner.Exemplary embodiments will now be described with reference to theaccompanying figures.

Flash Thermography Equipment

As shown in FIG. 1, the equipment for an infrared flash thermography(IRFT) system in accordance with one of the exemplary embodimentsdescribed herein comprises a flash lamp (source of light/heat), aflash-hood, flash power supply/trigger unit, flash duration controller,an infrared (IR) camera for capturing video images, camera dataacquisition electronics and a personal computer (PC). A PC is used forcontrolling the flash trigger for the flash lamp, data acquisition ofthe camera video data, data display, and post processing of the acquireddata. The flash-hood may be made from a sheet metal. One of the sixsides of the hood, which form a box, has a large opening so as to enablethe hood to be positioned over a test object. The side opposite to theopening has a hole in the center to provide a window for lens of the IRcamera that is mounted from outside of the hood. The IR camera isfocused at the test object (part) surface located at the hood opening.At least one flash lamp (two are shown in FIG. 1) is located within theinner wall of the hood in proximity to the IR camera. The flash lamp(s)directs illumination towards the hood opening where the test object islocated and, because the IR camera is positioned in such manner, withoutdirectly shining of light upon or into the camera lens. The hood acts asa housing that contains most of the intense flash.

One-Sided Flash Thermography Technique

If the test object can be accommodated inside the flash hood, then it islocated at the hood opening or slightly inside the hood. Otherwise, thepart is located slightly outside of the hood opening. A short duration(e.g. 3 msec), intense (12 kJ) flash is triggered using a computerkeyboard. The data acquisition is triggered a few seconds before theflash and it continues until the prescribed time. The camera provides asequence of IR images (or frames) called the data sequence (or digitalvideo) of the part surface taken at the chosen frame rate (e.g. 60 Hz or60 frames per sec). The intensity (numerical value) of each pixel in theimage is function of the surface temperature of the corresponding areaon the part at the time of image frame. The flash causes the surface towarm up slightly and the heat starts to dissipate rapidly. The surfacecools through thermal radiation, convection and conduction. It isassumed that the heat conduction within the part is the dominant heattransfer mode until the temperature gradients within the part becomesmall. At later times, the heat conduction is of the order of thecombined effect of heat convection and radiation. The IR dataacquisition and data analysis utilizes the thermal data in the shortduration immediately after the flash where the thermal dissipation isdominated by the heat conduction within the part.

The heat exchange across the boundaries due to the convection can beassumed to be zero if the Biot number (NBi=hL/k)<0.1. Consider anexample of ½ cm thick graphite/epoxy (k=0.64 W/mK) plate. Using h=10W/m^2, the Biot number is 0.078. Therefore, the heat conduction is thedominant mode of heat transfer in this example. Thinner parts tend toequalize the temperature within the part very quickly and haverelatively longer cooling time by heat loss to environment.

The IR Flash Thermography Anomaly Detection

Assume that the part is a plate made of a thermally isotropic materialwith constant thickness and it fits inside the hood. The plate issupported at the comers on insulating standoffs and the hood is orientedvertically. If it is assumed that the flash intensity is uniform overthe plate top surface, then the heat conduction will be in a directionnormal to the part surface in most of the acreage area (area away fromedges of the part and flash boundary). The heat is conducted uniformlyfrom the top surface to the bottom surface of the plate. The normal heatconduction will be obstructed by an anomaly such as a small roundgapping delamination at the center of a plate, as depicted in FIG. 2.The volume bounded by the anomaly on one side and the part top surfaceon the other side is called the heat trapping volume.

The top surface area surrounding the anomaly cools faster than the topsurface (footprint) area above the anomaly. The IR camera captures thesurface temperature image in terms of the pixel intensity and shows theanomaly as a hot spot (e.g. an area warmer than the surrounding area)which is about the size and shape of the anomaly footprint. The relativepixel intensity of the hot spot changes with the time. Deeper anomaliesappear at later times in the IR video compared to the near surfaceanomalies. After the appearance of an anomaly in the IR video, itsrelative pixel intensity continues to increase with time. The relativepixel intensity of the anomaly reaches a peak at a certain time and thenthe relative intensity decays until the indication area temperature andthe surrounding area temperature become equal. The part continues tocool down to the ambient temperature through heat convection andradiation.

Normalized Contrast

Let us define the normalized image contrast based on the pixel intensity(video raw data gray value) as follows. The contrast is also called thenormalized irradiance or the normalized pixel intensity contrast.

$\begin{matrix}{{C^{W} = \frac{{\Delta\; W} - {\Delta\; W_{ref}}}{{\Delta\; W} + {\Delta\; W_{ref}}}},} & (1) \\{{{\Delta\; W} = {W - W^{0}}},} & (2) \\{{{\Delta\; W_{ref}} = {W_{ref} - W_{ref}^{0}}},} & (3)\end{matrix}$where,C^(W)=normalized IR image contrast at time t,ΔW=change in pixel intensity of measurement ROI after flash,W=pixel intensity at measurement ROI at post-flash time t,W⁰=pixel intensity at measurement ROI before flash,ΔW_(ref)=change in pixel intensity of reference ROI after flash,W_(ref)=pixel intensity at reference ROI at post-flash time t, andW_(ref) ⁰=pixel intensity at reference ROI before flash.

The embodiments described herein may include steps of computing videodata consistent with equations such as equation 1 and other equations.By consistent it is meant that the computing includes the possibility ofterms that are approximate to, proportional to, or that can be derivedfrom the presented equations but do not need to be exactly the same orinclude the exact same letters, organization, and so forth. This isespecially true because it is well known that laws regarding handling ofequations allow for well known permutations and so forth. The generalmethod steps of normalized contrast video processing in accord withembodiments described herein are shown in FIG. 6.

FIG. 12 shows an example of contrast evolution video data extracted at aselected measurant ROI and reference ROI produced in accord with themethod shown in FIG. 6. The pixel intensity (raw gray value) startsbelow line “Y-Pos 29 34.” The first column of the column is frame time tin seconds, the second column is the pixel intensity of measurement ROIand the third column is pixel intensity of the reference ROI. Normalizedcontrast is computed for every pixel in the normalized contrast andderivatives (NCD) method and typically for all frames subsequent to theflash of heat using above equations.

Values in line “Diameter (in.)=0.0000, Depth (in.)=1.0000, Peak=0.0000,Peak Time (s)=0.0000, Scale Factor=1.0000, Offset=0.0000, BeginTime=0.0000, Slope=1.0000” are updated after analyzing the contrastevolution in CECA method, which is generally shown in FIG. 9.

Let us also define normalized temperature contrast based on convertingthe pixel intensity (raw data gray value) to temperature as follows.

$\begin{matrix}{{C^{T} = \frac{{\Delta\; T} - {\Delta\; T_{ref}}}{{\Delta\; T} + {\Delta\; T_{ref}}}},} & (4)\end{matrix}$ΔT=T−T ⁰,  (5)ΔT _(ref) =T _(ref) −T _(ref) ⁰,  (6)where,C^(T)=normalized IR image temperature contrast at time t,ΔT=change in pixel temperature of measurement ROI after the flash,T=pixel temperature at measurement ROI at post-flash time t,T⁰=pixel temperature at measurement ROI before the flash,ΔT_(ref)=change in the pixel temperature of reference ROI after flash,T_(ref)=pixel temperature at reference ROI a post-flash time t, andT_(ref) ⁰=pixel temperature at reference ROI before flash.

The normalized temperature contrast is computed for every pixel innormalized temperature contrast and derivatives (TCD) method using aboveequations as indicated in FIG. 8.

The definition of Converted Contrast C^(C) is covered separately.

Use of a reference test piece alongside the test object, both appearingin the field of view of camera, helps in the three contrast video imageprocessing (CVIP) methods where monitoring of thermography response isdesired. Here, the reference ROI can be chosen on the anomaly-free areaof the reference test piece. Also, if a programmed flaw exists in thereference test piece, the thermography response from the reference flawcan be used in monitoring thermography response.

Derivative Based Imaging Approaches

The embodiments described herein utilize first and second timederivatives of the Normalized Image Contrast (sometimes referred toherein as Normalized Contrast, Normalized IR image contrast, and thelike herein). As well, first and second time derivatives of thenormalized temperature contrast and converted contrast are disclosed.

Normalized Image Contrast 1st (order) time derivative is given by,C ^(W) ′=dC ^(W) /dt  (7)

Normalized Image Contrast 2nd (order) time derivative is given by,C ^(W) ″=dC ^(W) ′/dt  (8)

Normalized Temperature Contrast 1st derivative is given by,C ^(T) ′=dC ^(T) /dt  (9)

Normalized Temperature Contrast 2nd derivative is given by,C ^(T) ″=dC ^(T) ′/dt  (10)

Converted Contrast 1st derivative is given by,C ^(C) ′=dC ^(C) /dt  (11)

Converted Contrast 2nd derivative is given by,C ^(C) ″=dC ^(C) ′/dt  (12)

Any of the three Contrast formats 1st derivative is given by,C′=dC/dt  (13)

Any of the three Contrast formats 2nd derivative is given by,C″=dC′/dt  (14)

Where,

t=time corresponding the video frames.

These derivatives are computed for a plurality of frames and preferablycomputed for each relevant pixel in each frame of the contrast videogenerating derivative videos or at least for a plurality of frames.

Here, we define two different methods of computing the 1st derivatives.

Method 1A: The first method involves derivative of the raw contrastevolution data with smoothing during the calculation of derivatives.

Method 1B: Alternatively, the first method involves derivative of thesmoothed contrast evolution data using the smoothing during thecalculation of derivatives.

Method 2A: The variation of second method involves derivative of thecurve fit (non-simulation or empirical) to contrast evolution which maybe fitted to either raw or smooth data. The first derivativeC^(f)′=dC^(f)/dt and second derivative C^(f)″=dC^(f)′/dt are computedfrom the empirical curve fit equations C^(f)=f(t). Example of empiricalfit equation is given in eq. (41). Another example is given in FIG. 16.

Method 2B: Another variation of the second method involves derivative ofthe simulation fit to contrast evolution which may he fitted to eitherraw or smooth data. The simulation fit is also a curve fit (e.g.polynomial fit) in the contrast simulation (US Koshti, U.S. Pat. No.8,577,120 B1). The derivatives are computed from the simulation curvefit equations. The first derivative C^(f)′=dC^(f)/dt and secondderivative C^(f)″=dC^(f)′/dt are computed from the simulation curve fitequations C^(f)=f(t).

There are advantages as well as disadvantages for each method. Thesimulation fit and the non-simulation curve fit methods require that thecurve fit is established. If the fits cannot be established accuratelydue to non-ideal test conditions, low contrast value, choice of fitmodel etc., then the results would be in error. However, the derivativesare calculated from the fit function equations and therefore theresolution is excellent compared to the routines that use smoothedcontrast data and/or excessive smoothing to calculate derivatives. Thesmooth derivatives depend upon the length of the smoothing span (pointsor number of frames). Longer smoothing span provides better suppressionof the pixel temporal noise but it also flattens the contrast evolutionand reduces the time resolution. Depending upon the algorithm used,beginning portion of the evolution shows more effect of the smoothingspan, which may be used to your advantage in some situations.

Raw Contrast and Smooth Contrast Derivatives Method 1A

In order to illustrate this method, a test plate with back drilled flatbottom holes is used in the IR flash thermography data acquisition. FIG.3 shows of a photo of one possible non-limiting embodiment for testplate specimens A and B. In the non-limiting specimen A, all holes aredrilled to the same depth. There are five sizes of the diameter andthree holes for each size. Thus, there are five groups of threeidentical holes. Group 1: hole # 11, 13 and 15. Group 2: hole # 5, 7 and9. Group 3: Hole # 4, 6, and 8. Group 5: Hole # 10, 12 and 14. The holediameter increases from group 1 to group 5. Plate specimen B has fivedepths and 9 diameters. Holes in each row have same depth. See FIG. 4for depths and diameters of specimen B from FIG. 3.

IR flash thermography is performed on the front side of the plate. Thereference region of interest (ROI) is chosen near hole number 4 forspecimen A. The ROI size is 5×5 pixels. The reference ROI can be chosenon a separate plate of the same material that is kept next to the partand imaged concurrently with the part. The reference plate can bethicker or of the same thickness and no flaws should be present in thereference plate. Ideally, the reference ROI is never warmer than acreagearea of the part.

FIG. 13 shows the raw normalized contrast and the smooth derivativeevolutions for hole number 4 as an example. Two smoothing levels, i.e.,four point smoothing and the twelve point smoothing were used in thederivative images to illustrate the differences in the results. Longersmoothing span moves the location of the peak in the first derivative tothe right. The peaks in the contrast derivatives are with twelve pointsmoothing are shifted further to the right compared to those with fourpoint smoothing. The twelve point smoothing derivative evolutions aresmoother (less high frequency components) but flatter (less amplitude)than the four point smoothing derivative evolutions.

A number of derivative based images can be obtained. First, we createthe Normalized contrast video sequence data (C), the contrast firstderivative data sequence (C′) and the contrast second derivative datasequence (C″). We count the first postflash frame as frame 1. We canplay video of the three data sequences and select any frame thatprovides the desired image of the feature being detected. These contrastevolutions (C) are considered to be equivalent to the A-scan ofpulse/echo technique of ultrasonic testing and are called the contrastA-scans here. See FIG. 13.

In addition, several Extracted Images are possible as follows. Thecontrast evolution has a peak that can be reliably located by choosing acorrect time gate (window). This provides two possibilities for thecontrast feature images i.e. peak contrast and frame number at peakcontrast. The first derivative has a forward shifted peak due tosmoothing. This provides two possibilities for the contrast featureimages i.e. peak first derivative of contrast and the frame number ofpeak first derivative of contrast. The second derivative has a peak anda dip associated with the peak of first derivative. This provides manypossibilities for the contrast feature images i.e. peak of contrastsecond derivative, frame number of peak of second derivative ofcontrast, minimum of second derivative of contrast and frame number ofminimum of second derivative of contrast. In addition, we can combine(e.g. add, subtract, multiply and divide) these images to create newimages. These images are designed so that the anomaly data comes fromraw data frames close to the peak time. Earlier time images with timeclose to t50L (time at which contrast value is 50% of the peak value)and time that is less than the peak time (t_(peak)) provide bettersizing. Images beyond t^(peak) provide footprints that are larger thanthe size of the actual anomaly. Moreover, a desirable combination ofimages may suppress both spatial and temporal noise. Let us look at somederivative based images. Various images are shown below to illustratethe method of contrast based feature scans. Unless, mentioned, the timegate of frame 1 through frame 60 was used in the following images. Thetime gate can be used to choose section of the image frame for analysis,e.g., images preceding time gate, images after time gate or imagesbetween two time gates.

Various uses could include, for example only, a normalized contrast(C8), corresponding first derivative (C′8), and corresponding secondderivative (C″8) at frame 8 or an average normalized contrast (C_(av)),average of first derivative of normalized contrast (C′_(av)), andaverage of second derivative of normalized contrast (C″_(av)) from framenumbers 8, 9 and 10. The time or frame gate can be established fromframe 8 through 10. In this example, all such images would showanomalies i.e. anomalies, but visible contrast decreases with thederivatives due to reduction in amplitude of the correspondingevolutions. The values may be amplified appropriately by choosingdifferent set of frame images.

As another example, FIG. 14 shows peak normalized contrast (C_(max)),peak of the first derivative of the contrast (C′_(max)), and peak of thesecond derivative of the contrast (C″_(max)). All images show theanomalies. These images are considered to be similar to the ultrasonicC-scan amplitude images and are called the contrast value C-scans.

Another possibility is to show the frame number of the peak contrast(nC_(max)). This image is useful in assessing relative depth of theanomaly. This image is considered to be similar to the time-of-flightultrasonic C-scan and is called the contrast time of flight C-scan.

Another possibility is to show a comparison of peak the first derivative(C-scan) with different smoothing points. Twelve point smoothingprovides a less grainy image.

Another possibility is to show comparison of peak the second derivative(C-scan) with different smoothing points. Twelve point smoothingprovides a less grainy image. This would show clear detection of holeswith numbers 11, 13 and 15.

Another possibility is to show an image of the peak product time(C-scan).

Another possibility is to show images of the product of the peakcontrast with peak first derivative of the contrast (C-scan). Higherpoint smoothing provides a smoother and sharper image.

Another possibility is to show a (C-scan) image created by subtractingthe minimum of contrast from the peak contrast.

Another possibility is to show (C-scan) images created by subtractingthe minimum of first derivative of contrast from the peak of firstderivative of contrast. Higher point smoothing provides a smoother andsharper image.

Yet another possibility is to show (C-scan) images created bysubtracting the minimum of second derivative of contrast from the peakof second derivative of contrast. Higher point smoothing provides bettervisible contrast.

Yet another possibility shows (C-scan) images of a product of threequantities, i.e., the peak contrast, the peak of first derivative ofcontrast and the peak of second derivative of contrast. Higher pointsmoothing provides a smoother and sharper image although holes 11, 13and 15 would not be detected in this example.

Yet another possibility is to show (C-Scan) images of a product of threequantities i.e. the peak of second derivative of contrast, the peak offirst derivative of contrast and the negative of the minimum of secondderivative of contrast. Higher point smoothing provides a smoother andsharper image although holes 11, 13 and 15 are not detected.

Smooth Contrast and Smooth Contrast Derivatives Method 1B

The contrast evolutions can be smoothed using low-pass filtering toconvert the contrast video sequence data to the smoothed (filtered)contrast video sequence data.

One example of this is to show a smooth contrast and the smoothderivative (A-scan) evolutions for the region of interest at hole number4. Images can be created that are derived from the smoothed contrastvideo sequence data.

Another possibility is to show the peak contrast (Csmooth,max), the peakof first derivative of contrast (C′smooth,max), the peak of secondderivative of contrast (C″smooth,max) images. Normalized contrast andthe first derivative images in this example would show all anomalies.These images are considered to be similar to the ultrasonic C-scanamplitude images and called the contrast (value) C-scan images.

Another possibility is to show an image the frame number of the peakcontrast (nCsmooth,max). This image is useful in assessing relativedepth of the anomaly. Another possibility is to show an image from theframe number with the peak contrast. The peak contrast frame number inareas where no anomalies exist may not be desirable as peak contrastvalue is low. Therefore, peak contrast threshold of 0.1 could be chosen.One image could show a binary (value 1 or 0) image of peak contrastabove 0.1. The binary peak contrast image is multiplied with peakcontrast frame number image to provide a threshold modified image thatshows the peak contrast frame number above a peak contrast threshold of0.1. These images are considered to be similar to the ultrasonicC-scans.

Another possibility is to show a frame number of peak contrast of firstderivative (nC′smooth,max) image. This image is useful in assessingrelative depth of the anomaly. This image is considered to be similar totime-of-flight ultrasonic C-scan and is called contrast time C-scanhere. Determining frame numbers with the peak contrast for areas whereno anomaly exists is not of much value as the amplitude of the contrastevolution is low and derivatives can be dominated by the temporal noisewhich is higher for later frames due to decay of pixel intensity.

Yet another possibility is to show the image in the frame number of theminimum of contrast first derivative (nC′smooth,min) image. This imageis useful in assessing the relative depth of the anomaly. This image isconsidered to be similar to time-of-flight ultrasonic C-scan and iscalled contrast time C-scan here.

Another possibility is to show a minimum contrast of the secondderivative (C″smooth,min) image. This image is useful in assessingrelative depth of the anomaly. This image is considered to be similar tothe ultrasonic amplitude C-scan.

Yet another possibility is to show a peak product time (C-scan) which isuseful in assessing the anomaly gap thickness.

Yet another possibility is to show an image of a combined (C-scan) imagecalculated by multiplying peak contrast by peak of first derivative ofcontrast. The image shows that all holes and background noise isreduced.

Another possibility is to show an image with a combined (C-scan) imagecalculated by subtracting minimum of contrast from peak contrast. Theimage shows all holes but has speckled background noise.

Yet another possibility is to show a combined (C-scan) image calculatedby subtracting minimum of first derivative of contrast from peak firstderivative of contrast. The image shows all holes but has speckledbackground noise that makes detection of the smallest hole difficult.

Yet another possibility is to show a combined (C-scan) image calculatedby subtracting the minimum of second derivative of contrast from thepeak of second derivative of contrast. The image would show all holesbut has speckled background noise that makes detection of the smallesthole difficult.

Another image that could be generated would show a combined (C-scan)image calculated by multiplying the peak contrast, the peak of firstderivative of contrast and the peak of second derivative of contrast. Inthis example for the above described calibration standard would show allbut the smallest three holes but has very little background noise.

As another example, an image may be produced that shows a combined(C-scan) image calculated by multiplying the peak of second derivativeof contrast, negative of the minimum of second derivative of contrastand the peak of first derivative of contrast. The image would again showall but the smallest three holes but has very little background noise.

As another possibility, images could be generated that show normalizedcontrast images of peak contrast, standard deviation of peak ofnormalized contrast, peak of normalized contrast first derivative, andpeak of normalized contrast first derivative frame number (Time ofFlight C-Scan). Standard deviation is measure of noise in the data andis used to compute signal-to-noise ratio.

FIG. 15 shows the normalized contrast evolutions along a vertical linethrough holes 4 through 9 for the calibration standard discussed above.(See also B-scan with vertical line in FIG. 20.) The left image is inperspective view and the right image is a planer image. The contrastevolutions along a pixel line have been stacked or graphically presentedon a sequential manner. The staked contrast (or derivative) evolutiondisplay is considered to be equivalent to the B-scan image of theultrasonic pulse echo scanning technique and is called the contrastB-scan here. The images can be constructed from the derivative contrastsequences also.

Fitted Contrast and Fitted Contrast Derivatives Method 2

In the second method, we choose either a raw or smooth contrastevolution for the curve fit. For example, one possible image would showa raw contrast evolution and a five point (span) smoothing of theevolution. Next a suitable curve fit model is chosen (e.g. exponential,Fourier, polynomial etc.) and the curve fit is established. FIG. 16shows the curve fit equation, the fit data and the plot of the curve fitequation for each frame number obtained by the curve fit to the smoothcontrast data. Note that the curve fit extended into the negative valuesfor early frames. The raw data does not have negative values. Thecontrast evolution data is replaced by the curve fit coefficients (18 inthis case) in this method. Next the derivatives equations are written bytaking derivatives of the curve-fit equations. Using the derivativeequations, the derivatives are preferably computed for all frame numbersof interest using an appropriate computer application (e.g. MatLab).

FIG. 17 shows the derivative evolutions of the fitted normalizedcontrast evolution. The process is preferably repeated for each pixel ofthe normalized contrast evolution, e.g., for a plurality of frames,preferably all frames. The first derivative shows the peak of in thefirst frame and the second derivative shows a minimum in the first framewhich is different than would be the derivatives of method 1. Thepeaking in the first frame is due to the smooth extension of the curvefit (without inflection) to the first frame. However, depending upon thecurve fit, the frame rate and depth of the flaw, the curse fit may havean inflection point before the peak time. In such situations, a dip orlow in the first derivative could be observed. As another example, afitted normalized contrast evolution with peaks in the derivatives canbe produced which may show the contrast fit, the 1^(st) derivative andthe second derivative.

The image of the peak of second derivative of contrast, negative of theminimum of second derivative of contrast and the peak of firstderivative of contrast, discussed above, would show a contrast fit withthe early inflection and its derivatives which indicate early dip inderivatives and similarity in shape characteristics to the method 1derivatives. In this situation, all images defined for method 1 areapplicable. In case the derivatives do not show the initial dip, thenthe derivative images can be established at peaks of and minimums ofcontrast and its derivatives. In addition, frame number images atselected times (e.g. t50L, tpeak, t50R) can be plotted. All method 2images are also classified as the contrast A, B and C-scan images.

Converted Contrast Computation

A basis is for converted contrast. Consider a very thick plate of athermally isotropic material. The material has uniform, density,specific heat and thermal conductivity. It has a smooth surface (betterthan 63 rms) and uniform and high emissivity (>0.8). Before exposure tolight flash, the plate is also at equilibrium temperature with thesurroundings which is at constant temperature. When this plate isexposed to an intense light flash (i.e. a short duration heat pulse),the resulting temperature rise is given by,

$\begin{matrix}{{{{\Delta\;{T(t)}} = \frac{ɛ\; Q}{\beta\sqrt{\pi\; t}}},{t > 0}}{{where},}} & \left( 15 \right. \\{{\beta = \sqrt{k\;\rho\; c}},} & (16) \\{{H = \frac{Q}{\Delta\; t}},} & (17)\end{matrix}$

where,

-   -   ΔT=temperature change from pre-flash temperature on front        surface (° C., K or ° F.),    -   t=time (sec) measured after flash, flash time is zero and is not        part of the equation.    -   β=thermal effusivity of layer above the anomaly, (cai-cm²-°        C.1-sec^(−1/2), kcal-m⁻²-K⁻¹-sec^(−1/2) or BTU-ft⁻²-°        F.⁻¹-hr^(−1/2)),    -   c=specific heat (cal-gm⁻¹-° C.⁻¹, kcal-kg⁻¹-K⁻¹ or BTU-lbm⁻¹-°        F.⁻¹),    -   ε=emissivity of the flashed surface,    -   Δt=flash time (sec),    -   H=average heat flux incident on the surface (cal-cm⁻²sec⁻¹,        kcal-m⁻²sec⁻¹ or BTU-ft⁻²hr⁻¹),    -   Q=total heat incident on the test object per unit surface area        (cal-cm⁻², kcal-m⁻² or BTU-ft⁻²),    -   k=thermal conductivity, (cal-cm⁻¹-° C.⁻¹sec⁻¹, kcal-m⁻¹-K⁻¹        sec⁻¹ or BTU-ft⁻¹-° F.⁻¹hr⁻¹),    -   ρ=material density (g-cm⁻³, kg-m⁻³ or lbm-ft⁻³).

The above equation assumes that the surface temperature is affectedsolely by diffusion of heat within the test object by conduction. Heatconduction at the center point is assumed to be in a direction normal tothe top surface. Flash from the photographic flash lamps is assumed tobe an approximation of an instantaneous heat pulse.

The above equation is not valid during the flash duration. Although mostof flash energy of is expended within the selected flash durationsetting (e.g. 3 msec), some afterglow decay may linger in first coupleof frames (˜15-30 msec). Thus, after decay of the afterglow, the aboveequation may provide a better match with measured surface temperature.

IR camera, used in flash thermography, typically measures test surfaceirradiance in terms of the pixel intensity and not surface temperature.The related art of U.S. Pat. Nos. 9,066,028 and 8,577,120 describes howpixel intensity could be converted to surface temperature in flashthermography.

Here, we make an assumption that the part has high emissivity and thatpixel intensity is proportional to a fixed power (e.g. a positiveinteger n=4) of the absolute temperature based on the Stefan Boltzmanlaw.W=λ ^(N)  (18)W ⁰=λ(T ⁰)^(n)  (19)

Here, λ is the Stefan Boltzman constant. Based on the above assumption,we can derive that change in pixel intensity is proportional to a fixedpower of change in surface temperature or vice versa. Let us takederivative of Eq. (18).dW=λnT ^(n−1) dT.  (20)

Combining Eq. (18) and Eq. (20) we get,

$\begin{matrix}{{\frac{\Delta\; W}{W} \cong {n\;\frac{\Delta\; T}{T}}},} & (21) \\{{\Delta\; T} \cong {\frac{T}{nW}\Delta\; W}} & (22)\end{matrix}$

Combining Eq. (15) with Eq. (22) we get,

$\begin{matrix}{{{\Delta\; W\sqrt{t}} \cong {\frac{ɛ\; Q\; n}{\beta\sqrt{\pi}}\frac{W}{T}}},} & (23) \\{{{\Delta\; W\sqrt{t}} \cong {\frac{ɛ\; Q\; n}{\beta\sqrt{\pi}}\frac{W^{0}}{T^{0}}}},} & (24)\end{matrix}$

On right hand side of Eq. (23), we have quantities that are constantexcept term W/T. Ratio W/T is not constant during flash and earlierportion of the flash afterglow. Once afterglow has subsided, the ratioW/T is becomes approximately constant with time if it is assumed thatvery small changes in the temperature occur after the afterglow.Therefore, from Eq. (23), we conclude that in isotropic materials inreference area,

$\begin{matrix}{{\Delta\; W_{ref}} \propto {\frac{1}{\sqrt{t}}.}} & (25)\end{matrix}$

Therefore, we can rewrite Eq. (25) genetically as,ΔW _(ref) ≅Mt ^(s).  (26)

where,

M=a multiplier that depends on the highest pixel intensity value whichoccurs right after flash. It is dependent on incident flash energy Q,emissivity ε and effusivity β according to Eq. (23). If materialproperties are assumed to be constant from shot to shot on the samepart, only incident flash energy Q is likely to vary due to variation ingeometric part/flash-hood set-up and variation in the flash energyproduced; and

s=slope or time exponent. It is property of the material. Based on Eq.(23), for isotropic materials it is close to −0.5.

Based on above observation, the converted contrast method involves anempirically derived transformation or multiplier function f(t), whenmultiplied to relative pixel intensity of the reference region ofinterest yields a constant baseline value B.

Definition of the converted contrast is given as,C ^(C) =ΔWf  (27)

such that the converted contrast at reference region has a constantvalue B.C _(ref) ^(C) =ΔW _(ref) f≅B  (28)

Therefore, the multiplier functions are chosen so that,

$\begin{matrix}{f \cong \frac{B}{\Delta\; W_{ref}}} & (29)\end{matrix}$

Another way to determine the multiplier function is to fit a curve toreference relative pixel intensity,

$\begin{matrix}{f = \frac{B}{F_{\Delta\; W_{ref}}}} & (30)\end{matrix}$where,F_(ΔW) _(ref) =curve fit function to relative intensity of referencepixel. Therefore, Eq. (26) can be written as,ΔW _(ref) ≅F _(W) _(ref) =Mt ^(s)  (31)

Multiplier function in this case is,

$\begin{matrix}{{f(t)} = {\frac{B}{\Delta\; W_{ref}} = \frac{B\; t^{- s}}{M}}} & (32)\end{matrix}$

The baseline value, B can be set to approximately 1 or 100. Convertedcontrast is not considered to be normalized. In computing the convertedcontrast, relative pixel intensity is multiplied by a function. There isno subtraction or division by a small number. Normalized contrast hassubtraction of reference relative pixel intensity and division by smallnumbers which amplify noise for small contrast values, therefore needingsmoothing. Converted contrast computation does not have above factorsthat add noise during computation.

Using definition of the converted contrast given in Eq. (27), andnormalized contrast Eq. (1), a relationship between the two can berewritten as,

$\begin{matrix}{C^{W} = {\frac{C^{c} - C_{ref}^{c}}{C^{c} + C_{ref}^{c}} \cong \frac{C^{c} - B}{C^{c} + B}}} & (33) \\{C^{c} \cong {B\;\frac{1 + C^{W}}{1 - C^{W}}}} & (34)\end{matrix}$

Thus, we can compute normalized contrast from converted contrast andvice versa. Note that the converted contrast computation is much simplerthan the normalized contrast and depending upon the multiplier function,the converted contrast can have high values. Higher values areadvantageous in manipulating plots. Because computation of the convertedcontrast introduces some measurement errors (e.g. varying W/T duringafterglow), we may have certain advantages and disadvantages.

As evident from the Eq. (33) and (34), shapes of the normalized contrastand the converted contrast are very similar except for short durationimmediately after flash. Thus, characteristics of derivatives ofnormalized contrast and converted contrast are very similar and allnormalized contrast imaging and contrast derivative imaging are alsoapplicable to the converted contrast video sequence data and convertedcontrast derivative imaging.

The reference pixel relative intensity function modeled in Eq. (31) canbe determined using the IR raw data video sequence of the part. Chooseanomaly-free area of part as reference ROI. Choose a window of framesbeyond tail end of the afterglow, beyond texture effects but beforeearly detection time7 corresponding to the bottom surface detection.Plot the reference relative pixel intensity with time in linear andlog-log scales. Compute the exponent s as slope of the selected portionof the log-log reference relative pixel intensity plot using followingequation preferably from log-log plot.

$\begin{matrix}{s = {\frac{d\;{\ln\left( {\Delta\; W_{ref}} \right)}}{d\;{\ln(t)}} = {\frac{d\left( {\Delta\; W_{ref}} \right)}{d\; t}\frac{t}{\Delta\; W_{ref}}}}} & (35)\end{matrix}$

The chosen linear portion of the log-log plot for calculating slope sbeyond decay of afterglow as visually observed in the plot. Compute themultiplier M using following equation.

$\begin{matrix}{M = {e^{({{l\;{n{({\Delta\; W_{ref}})}}} - {s\; l\;{n{(t)}}}})} = {\frac{\Delta\; W_{ref}}{t^{s}}.}}} & (36)\end{matrix}$

Compute many estimates of multiplier M over the selected range ofreference relative pixel intensity. Use average of these estimates asthe value for multiplier M. Multiplier M is primarily affected by flashintensity. A higher flash energy result in higher M. Higher flash energyreduces noise in the data. Finally, choose value of B. If we chooseB=100, then

$\begin{matrix}{C^{c} = \frac{100\left( {\Delta\; W} \right)t^{- s}}{M}} & (37)\end{matrix}$

General equation for converted contrast is,C ^(C) =f(ΔW)=Gt ^(n)(ΔW)  (38)Where,G=a constant, G may be assumed to be 1.n=positive fractional number.

EXAMPLE 1 Converted Contrast Method Using f(t)=t0.25

In this example, IR flash thermography is performed on front side ofplate specimen 1. Reference region of interest (ROI) is chosen near holenumber 4. The ROI size is 5×5 pixels. The function f(t)=t0.25 was chosenin this example. Units of the converted contrast in this situation wouldbe pixel intensity gray value multiplied by sec0.25.

FIG. 18 shows raw contrast and smooth derivative evolutions for holenumber 4 as an example. Top three plots are for direct computation ofnormalized contrast and its derivatives. The bottom three plots are forthe converted contrast and its derivatives. The raw normalized contrastand the raw converted contrast differ for the first three frames.Normalized contrast computation reduces the effect of the afterglow. Thematerial emissivity is about 0.8 and 20% of the afterglow is reflectedby the part giving higher than expected pixel intensity until theafterglow decays sufficiently. In normalized contrast computation,relative pixel intensity evolution of the reference ROI is subtractedfrom the same for the measurement pixel. This subtraction reduces theeffect of higher relative pixel intensity in the contrast due to theafterglow. The reference ROI contained 25 pixels, thereby reducing theeffect of the texture noise in the reference ROI. The surface texturealso affects effect of the afterglow providing relative increase ordecrease in pixel intensity values. There is no compensation for thehigher or lower relative pixel intensity due to afterglow/texture effectin computation of converted contrast and a peak is observed in the firstpost flash frame. The converted contrast decays with decay of theafterglow and the diffusion of texture indications. It may reach a valueequal to the baseline converted contrast value provided the subsurfaceanomaly is relatively deep. At the early detection time, the convertedcontrast starts increasing. Thus, in order for the converted contrast toreach its constant baseline value before the peak time, there should besome time interval between end of the effect of the afterglow/textureand the early detection time. Note that the shapes of the correspondingfirst and second derivatives of normalized contrast and convertedcontrast are similar.

FIG. 19 shows an example of low-pass filtering of converted contrastevolution from FIG. 18. Filtering can remove the afterglow/textureeffect. If there is no need to evaluate any texture anomalies, then suchfiltering is useful as it reduces the pixel noise.

FIG. 20 shows frame 4 images from converted contrast, converted contrastfirst derivative and converted contrast second derivative datasequences. In this example, all holes are detected. Converted contrastimage shows a lot of surface texture. The derivative images also showsome texture. These images are considered to be similar to the gatedamplitude ultrasonic C-scans and are called the contrast value C-scans.

Another possibility is to show the images from the frame number of thepeak contrast from normalized contrast video sequence data and from theconverted contrast video sequence data. In other words the image of themaximum contrast frame number from the normalized contrast can becompared with the image of the maximum contrast frame number from theconverted contrast. These images look similar and are useful inassessing relative depth of the anomaly. In this case, these images areconsidered to be similar to the time-of-flight ultrasonic C-scan and arecalled the contrast time (of flight) C-scans.

Another possibility is to show images of peak product time fornormalized contrast and for the converted contrast. The Peak producttime is equal to the maximum contrast×the maximum contrast frame numberto produce an image from the normalized contrast and from the convertedcontrast. In some cases, the images may look very similar. The samesmoothing or no smoothing may be utilized.

FIG. 21 shows the converted contrast at a single pixel at the center ofreference ROI near hole # 4. The initial high contrast is due to theafterglow/texture effect. After first three frames the contrast levelsout more or less. The baseline level (e.g. B) of this portion of thecontrast is approximately equal to 255 (pixel intensity×sec0.25).

Another possibility is to show a B-scan representation of the convertedcontrast for a vertical line passing through holes 4 and 9. In thiscase, the B-scan shows a lot of afterglow/texture peaks. The largest ofthese are seen on hole number 5 and number 7. The image is similar tothe B-scan representation of the normalized contrast of FIG. 15. Theconverted contrast can be used to bring out the near surface anomalies.

FIG. 22 shows an example of computation of normalized contrast from theconverted contrast for the same contrast evolution used in FIG. 18 andFIG. 19 using a value of B equal to 255. The agreement between directcomputation of normalized contrast using Eq. (1) and computation ofnormalized contrast from converted contrast is good except for the firsttwo frames where the afterglow/texture effect is evident. The normalizedcontrast provided a smooth variation of slope in beginning framesimplying that the afterglow/texture effect is mainly dominated by theafterglow and not by the texture in this example.

EXAMPLE 2 Converted Contrast Method Using Curve Fitted MultiplierFunction

Another possibility is to show a plot of relative pixel intensity at areference ROI with respect to time and its curve fitted function. Aratio of polynomials is chosen for the fit function, however Gaussianand power series equations also showed good fits. In this case,F(t)=(p1*t+p2/(t²+q1*t+q2), which gives the equation for thecoefficients p1, p2, q1 and q2. In one example, the cofficents aredetermine with 95% confidence bounds. Although, use of a statisticallyfitted multiplier function yields better results, it requires additionalstep of fitting curve to the relative intensity of the reference ROI andresults in many coefficients. Using simply the fractional power of timefor the multiplier function is very attractive as only one coefficient(e.g. n) is used, which could be easily determined for a given materialtest set-up and could be used on the same material. This, provides useof the implied reference rather than a chosen ROI for the reference. Thefitted multiplier functions also may be used in similar manner uponverifying their applicability.

Another possibility is to show a plot of converted contrast at the sameselected reference pixel as discussed above for the example of thefitted normalized contrast evolution with peaks in the derivatives. Theplot is a line about horizontal with mean close to 1.0 as expected.Here, B=1 in Eq. (31).

Another possibility is to show a comparison of converted contrast andnormalized contrast evolution plots, for example, at hole number 4pixel. In one case, the plot is very close to the normalized contrastusing the direct method using Eq. (1). FIG. 22 graphically shows acomparison on the same graph.

FIG. 23 shows example of reference region of interest (ROI) evolution,line fit, slope and multiplier for specimen 2.

FIG. 24 shows extracted converted contrast images of peak contrast,standard deviation of peak contrast, peak of first derivative ofcontrast, and frame number of peak of first derivative (time of flightscan) for specimen 2 using slope s and multiplier M given in FIG. 23.

Temperature Contrast Computation

Computation of surface temperature and normalized temperature contrastduring flash thermography utilizes a foil and tape set-up as describedin U.S. Pat. Nos. 8,577,120 and 9,066,028 and shown generally in therepresentation of FIG. 25. Here, metallic foils are used to monitorcamera side diffused reflection. A black color tape, affixed to thepart, is used get emissive irradiance primarily before flash. The partsurface temperature at the tape before flash is independently measuredand input in this method. An input panel display for temperaturemeasurement is shown in FIG. 25. Emissivity of part surface is alsoprovided as input. ROI's for tape and foil are established. There aretwo ROI's for foil and one for tape. Average pixel intensity evolutionfrom the two foil ROI's is used. Average pixel intensity evolution fromwithin the tape ROI is used. These input quantities are used to computetemperature evolutions preferably at each pixel as given herein.

From surface temperature, temperature rise is computed as,T _(rise) =T−T ⁰.  (39)

Also simple contrast is computed as,C _(simple) ^(T) =T−T _(ref).  (40)

FIG. 26 shows an example of normalized temperature contrast evolution,smoothed or processed normalized temperature contrast evolution and itsderivative evolutions at a selected measurement pixel.

Another possibility is to show temperature contrast extracted images. Inother words, Normalized Temperature Contrast Frame Images can be shownfor surface temperature, standard deviation of surface temperature,temperature rise, and temperature difference. In one example, this mayinclude an image for each of the maximum raw temperature contrast, themaximum raw temperature contrast standard deviation, the maximumtemperature contrast 1^(st) derivative, and the maximum temperaturecontrast 2^(nd) derivative.

FIG. 27 shows Normalized Temperature Contrast Extracted Images of peakcontrast, standard deviation of peak contrast, and peak of firstderivative of contrast, and peak of second derivative of contrast.

Normalized Contrast Calibration Method

As discussed herein, the embodiments described herein show that pointmeasurement of normalized contrast on a round anomaly can be used indata analysis. Further it was shown that the entire flash thermographydata sequence can be converted to contrast video sequence data and usedfor data analysis. Here, an empirical method is provided to calibratenormalized contrast response as a function of size of artificialanomalies in the shape of round flat bottom holes or embedded gaps. Oncecalibrated, the calibration data can be used to assess depth ofanomalies during flash thermography inspection. The calibration dataprovides six properties of contrast evolution namely the peak time andpeak contrast (amplitude), time scale factor, offset time, begin time,and slope (different from slope factor) as a function of depth anddiameter of the anomalies in a calibration reference standard.

A calibration standard with flat bottom holes (or embedded gaps) withdesired diameter (D) and depth (d) values is prepared. FIG. 3 shows aschematic of such a standard with flat bottom holes. FIG. 4 shows aschematic of the standard. FIG. 5 shows a flash thermography raw dataimage for a selected frame or time within the data sequence. The imageis created by overlapping 6 shots. The holes are arranged in five rows.There are 9 holes in each row with 9 different diameters. Thesediameters are same in each row. Depth of flat bottom holes in a givenrow is same. The depths and diameters create a 9 by 5 grid of 45diameter-depth pairs. Measurement ROI for each hole is chosen in thecenter of image of each hole. The reference ROI is chosen in thevicinity of image of the hole at a location that provides an optimalcontrast evolution profile. See FIG. 5. A text file with measurement andreference pixel data is extracted from the data sequence for each holeindication. See FIG. 12 for a sample contrast data text file. The textfiles and their associated flat bottom hole diameter and depth valuesare used to generate data for the calibration.

First each extracted contrast data file is displayed as a normalizedcontrast evolution. Next the six contrast evolution parameters arecalculated by analyzing the contrast evolution. FIG. 28 shows a computermethod panel for assessment of the contrast parameters and theassessment of the depth. The contrast evolution file is opened using amethod from the computer screen shown in FIG. 28. The method alsoprovides a curve fit where a χ2 Chi-square probability densitydistribution is fitted to the contrast evolution. See FIG. 29 forChi-Square probability density function for various values of k. Here,it is assumed that there is a single peak to the distribution. Thedistribution starts with zero value for zero time and reaches zero valueat a long time or infinity.

A χ2 (or Chi-square) probability density function (PDF) distributionchosen for the fit is given by.

$\begin{matrix}{{f\left( {x;k} \right)} = \frac{x^{{({k/2})} - 1}e^{{- v}/2}}{2^{k/2}{\Gamma\left( \frac{k}{2} \right)}}} & (41) \\{{{for}\mspace{14mu} x} \geq {0\mspace{14mu}{and}}} & \; \\{{f\left( {x;k} \right)} = {0\mspace{14mu}{{otherwise}.}}} & \;\end{matrix}$

Here, Γ(k/2) denotes Gamma function. The Gamma function is given by,

$\begin{matrix}{{\Gamma(z)} = {\int_{0}^{\infty}{e^{- l}t^{z - 1}d\; t}}} & (42)\end{matrix}$

Although, the PDF distribution for various values of k providessufficient variability in shapes that look very similar to realnormalized contrast evolutions, the PDF distribution cannot fit theentire length of the contrast evolution for many cases of contrastevolutions. Distribution fits can be improved by increasing the numberof coefficients used in a chosen fit equation. However, for oneembodiment the number of distribution coefficients is kept to four tocreate a simpler approach. The four contrast parameters are peak time,peak contrast, time scale factor, and offset times which completelydefine the Chi-square PDF distribution. Normally, amplitude and peaktime are not used as the input parameters to the Chi-squaredistribution. Therefore, an algorithm is used to determine theChi-square coefficients including k that provide the desired parametervalues. The fit is allowed to be shifted, therefore zero offset time isused to achieve the shift.

Contrast evolution slope of the fit is calculated at the 50% peak pointin rising side of the contrast and is indicated by a ‘o’. A slope lineis drawn at the 50% of peak of fitted contrast. Begin time is usedindicate the early detection time of fitted contrast evolution. It isgiven by starting point of the slope line at zero value of normalizedcontrast and is indicated by ‘*’. Slope line upper end has value of peakcontrast and it is indicated by a ‘o’ as well. Slope value is not usedas an input. Begin time input of slope line is visually observed in theplot and adjusted accordingly. Slope value is calculated automatically.

A Match Peak sub-method (FIG. 30, button 3002) allows matching of peakpoint of fit with that of the contrast evolution for a given scalefactor, offset time and begin time.

Alternatively, a Match Shape sub-method (FIG. 30, button 3006) allowsmatching of shape of fit with that of the contrast evolution for givenpeak contrast, peak contrast time, offset and begin time. FIG. 31 showsan example of computation of difference between simulation fit andnormalized contrast evolution as a function of time scale factor. Thisplot is provided by Match Shape sub-method. A V shaped curve indicatesthat a minimum in the difference has been reached and the correspondingtime scale factor is close to the optimal value and this value isautomatically updated.

A User Input method (FIG. 30, button 3008) allows updating of the fitusing all five displayed values of the parameter.

Each of peak contrast, Peak Time, Time Scale, Offset Time and Begin Timeslider control methods (FIG. 30, slider controls 3010) allow adjustmentof the corresponding parameter and instant update of the fit.

FIG. 30 shows method of matching a fit curve to a normalized contrastevolution. Here simulation is matched.

The contrast evaluation method shown in FIG. 28 is used to update theevolution data file with diameter depth and the six contrast parameters.See FIG. 12. To complete calibration, each of the 45 contrast evolutionfiles are evaluated and their files are updated.

The contrast evaluation method reads the calibration contrast evolutiondata files as a batch and creates a database internally. See Batch Filessub-method in FIG. 30, item 3012.

The data values of calibration database are given in FIG. 32. Thecalibration database has 8 columns of data and 45 rows. There is one rowfor each contrast evolution corresponding to each calibration hole.First two columns are for diameter and depth. The last six columns arefor the six contrast parameters. The contrast evaluation method displaysthe calibration data in six 3D plots with fitted surfaces. These areused to detect any unexpected trends and make corrections. Smoothersurface fits are expected. FIG. 33 shows Calibration Data and fittedsurfaces for six normalized contrast evolution parameters.

In order to evaluate a contrast evolution from an anomaly indication,first the data file is loaded using File sub-method and all calibrationfiles are preferably read using the Batch Files sub-method. See FIG. 30.The contrast evolution is evaluated by fitting a curve and slope linethrough the curve. Diameter or width of the indication should bemeasured in Frame images or Extracted images. The six contrast evolutionparameters and the diameter are updated in the contrast evolution fileand using sub-method of Evaluate Depth, depths are assessed anddisplayed in bottom left. Up to six estimates, one from each parameter,are obtained by interpolating calibration data. See FIG. 32 and FIG. 33for calibration data. See FIG. 30, box 3014 bottom left for the depthestimates. A seventh estimate is called Interpolation estimate and isbased on interpolation of the previous, up to six, estimates usingpredicated peak contrast from these estimates to give a depth that givesthe observed peak contrast. If the six estimates are identical thenthere is no interpolation. The final interpolation increases confidencein depth estimation. See FIG. 30, box 3014 bottom left.

Note the reference standard may have other types of flaws created bypre-curing known size areas at selected plies within composites and thencuring the entire part. These create delamination like anomalies ofknown size and depth.

Here, round flat bottom holes are used in a calibration standard. Thestandard can be generated from embedded gaps or delaminations withrectangular or other shapes with fixed length to width aspect ratio. Inthis situation, the width of the flaw would be used in place of thediameter. It is recommended to maintain the same gap value in a givencalibration file. The calibration flaws should replicate the flaws to beevaluated, especially in shape and gap thickness, to provide betterassessment of depth of the anomalies and POD analysis.

Other Image and Data Analysis Methods

Because of many options of image data analysis, it is desirable toorganize and integrate methods for computation. Many contrast methodsgiven here, except POD analysis, can be integrated in a Contrast methodmain panel shown in FIG. 34. Numerous options for different types ofcontrast methods have been provided in this application and previouspatents that can be implemented by a computer program that can pull upthese procedures. In one application, the methods below are implementedfrom a computer screen. Accordingly, the embodiments described hereinprovide for processing steps for the Contrast methods that include thoseand other image and data analysis methods some of which are listedbelow.

1. Method of Smoothing and Filtering provides choice of Smoothingpoints, and Filters (e.g. moving average).

2. Method of Evolutions provides extraction of contrast evolution forselected pixel as a data file. See FIG. 12. Normalized contrastevolutions and its derivatives at a selected pixel as shown in FIG. 26.Evolution Evaluation and Calibration method. See FIG. 28.

3. Method of Contrast Computation provides three options, i.e.,Normalized contrast, Converted contrast and Normalized temperaturecontrast.

4. Analysis method in main panel provides three options, i.e., Frameimages, Extracted images and Analyze saves images.

Video frame images are called Frame Images herein. A frame number isassociated with each frame image. The Frame images method is integratedin a Frame Images panel for a computer software program implementing themethods described herein.

5. Frame Images panel or screen in the computer program has FrameImages, Analysis sub-methods, and Options for Frame Images. For examplea video sequence may be chosen for a frame image. From this a framenumber may be chosen to display and/or adjust contrast or to play thevideo and adjust the contrast. Additional steps may involve evolutionsand profile, edge detection, area statistics, length measurement, andstandard deviation. A choice of video sequences for frame images mayinclude:

-   -   1. Raw Data (Gray Scale)    -   2. Normalized Contrast    -   3. Processed or Smoothed Contrast    -   4. Contrast 1^(st) Derivative    -   5. Contrast 2^(nd) Derivative    -   6. Temperature, K—applicable for Normalized Temperature        processed data only    -   7. Temperature rise, K—applicable for Normalized Temperature        processed data only    -   8. Simple contrast, K—applicable for Normalized Temperature        processed data only

6. Analysis method provides Contrast (normalized, converted contrast andtemperature) evolutions and its derivatives at a selected pixel as shownin FIG. 26 for temperature contrast example.

7. Analysis methods under Frame Images, Extracted Images or Analyzedsaved have three options i.e., Edge Detection, Profile and AreaStatistics.

Images extracted by scanning or searching for values from multipleframes are called Extracted Images. Images derived from Extracted Imagesare also called Extracted Images. A single frame number is notassociated with extracted image. Extracted image method is integrated inan Extracted Images Panel. Extracted Images options are discussedherein.

Possible non-limiting examples of extracted image choices include:

-   -   1. Maximum raw contrast    -   2. Maximum raw contrast frame number    -   3. Maximum smoothed contrast    -   4. Maximum smoothed contrast frame number    -   5. Maximum first derivative    -   6. Maximum first derivative frame number    -   7. Maximum second derivative    -   8. Maximum second derivative frame number    -   9. Peak contrast×peak time    -   10. (Maximum of 1^(st) derivative contrast)×maximum contrast    -   11. Maximum contrast−minimum contrast    -   12. Maximum of 1^(st) derivative contrast−minimum of 1^(st)        derivative contrast    -   13. Maximum of 2^(nd) derivative contrast−minimum of 2^(nd)        derivative contrast    -   14. Maximum contrast×maximum of 2^(nd) derivative×maximum of        1^(st) derivative    -   15. Maximum of 2^(nd) derivative×minimum of 2^(nd)        derivative×maximum of 1^(st) derivative    -   16. Masked images    -   17. Standard deviation of any image    -   18. Selected groups of frames of C^(W), C^(W)′ or C^(W)″    -   19. Selected frame numbers for C^(W), C^(W)′ or C^(W)″    -   20. Selected frame numbers associated with a peak of C^(W),        C^(W)′ or C^(W)″    -   21. Selected frame numbers associated with values above a        threshold value for C^(W) or C^(W)′ or C^(W)″    -   22. selected frame numbers of C^(W) or C^(W)′ or C^(W)″ that are        associated with values from a vertical or horizontal pixel line    -   23. Selected frame numbers of C^(W), C^(W)′ or C^(W)″ that are        associated with values from a selected pixel.    -   24. Selected groups of frames C^(C), C^(C)′ or C^(C)″    -   25. Selected frame number of frames for C^(C), C^(C)′ or C^(C)″    -   26. Selected frame number associated with a peak of C^(C),        C^(C)′ or C^(C)″    -   24. Selected frame number associated with a peak of at least one        of C^(C), C^(C)′ or C^(C)″    -   25. Selected frame numbers associated with values above a        threshold value for C^(C), C^(C)′ or C^(C)″    -   26. One or more selected frame numbers of C^(C), C^(C)′ or        C^(C)″ that are associated with values from a selected vertical        or horizontal pixel line    -   27. Selected frame numbers of C^(C), C^(C)′, C^(C)″ that are        associated with values from a selected pixel    -   28. Selected groups of frames of C^(T), C^(T)′ or C^(T)″    -   29. Selected frame number of frames for C^(T), C^(T)′ or C^(T)″    -   30. Selected frame number associated with a peak of C^(T),        C^(T)′ or C^(T)″    -   31. Selected frame numbers associated with values above a        threshold value of C^(T), C^(T)′ or C^(T)″    -   32. Selected frame numbers of C^(T), C^(T)′ or C^(T)″ that are        associated with values from a vertical or horizontal pixel line    -   33. Selected frame numbers of C^(T), C^(T)′ or C^(T)″ that are        associated with values from a selected pixel    -   34. Normalized contrast frame number and contrast derivatives        including for example maximum contrast frame number (no        smoothing), maximum of 1^(st) derivative (4 point smoothing) and        maximum of 1^(st) derivative (12 point smoothing) or other        smoothing settings.    -   35. Maximum normalized contrast 2^(nd) derivative and peak        product times including non-limiting examples of maximum of        contrast 2^(nd) derivative (4 point smoothing), maximum of        contrast 2^(nd) derivative (12 point smoothing) and peak product        time=maximum contrast×maximum contrast frame number.    -   36. Normalized contrast images derived from other images such as        but not limited to Maximum of contrast×maximum of 1^(st)        derivative (taken with different smoothing) and maximum        contrast−minimum contrast.    -   37. Normalized contrast images derived from other images may        also include for example maximum 1^(st) derivative−minimum        1^(st) derivative taken with different smoothing.    -   38. Normalized contrast images derived from other images may        also include maximum 2^(nd) derivative−minimum 2^(nd) derivative        taken with different smoothing.    -   40. Normalized contrast images derived from other images may        also include maximum of contrast×maximum of 1^(st)        derivative×maximum of 2^(nd) derivative taken with different        smoothing.    -   41. Normalized contrast images derived from other images may        also include maximum of 2^(nd) derivative×minimum of 2^(nd)        derivative×maximum of 1^(st) derivative taken with different        smoothing.    -   42. Maximum normalized contrast and maximum normalized contrast        derivatives including maximum contrast, maximum contrast 1^(st)        derivative, and maximum contrast 2^(nd) derivative.    -   43. Maximum normalized contrast frame number, mask image, and        masked frame number image such as maximum contrast frame number,        Binary (mask) image of maximum contrast at threshold=0.1, and        these items multiplied to provide a masked maximum contrast        frame number.    -   44. Normalized contrast frame number and normalized contrast        derivatives including maximum contrast 1^(st) derivative frame        number, minimum contrast 1^(st) derivative frame number and        minimum contrast 2^(nd) derivative.    -   45. Extracted normalized contrast image derived from other        images may include peak product time=to maximum contrast×maximum        contrast frame number, maximum of contrast×maximum of 1^(st)        derivative contrast and maximum contrast−minimum contrast.    -   46. Extracted normalized contrast image derived from other        images may include maximum 1^(st) derivative contrast−minimum        1^(st) derivative contrast, maximum 2^(nd) derivative        contrast−minimum 2^(nd) derivative contrast, and maximum of        contrast×maximum of 1^(st) derivative×maximum of 2^(nd)        derivative.    -   47. Extracted normalized contrast image derived from other        images may include maximum of 2^(nd) derivative×minimum of        2^(nd) derivative×maximum of 1^(st) derivative.    -   48. Extracted normalized contrast images of maximum contrast,        standard deviation, of maximum normalized contrast, maximum        normalized contrast 1^(st) derivative, and maximum normalized        contrast 1^(st) derivative frame number (TOF—Time of flight        scan)

Distance or length measurement method using a line superimposition isapplicable where 2D images are shown. See FIG. 35 where the line isindicated on an image and its length is indicated at the bottom right.The computer is operable to measure the length of the line. If desired,this line could be automatically produced. For example, automated edgedetection as discussed herein could be utilized to locate the edges andthe line could be generated and measured automatically in any desireddirection.

8. Distance measurement is also available in Main panel, Frame imagespanel, Extracted images panel, and Analyze saved images panel. Thedistance measurement can be used to measure flaw size by dragging endpoints of the line to selected edge points of flaw indications.

Within the three data analysis methods there are other sub-methodsincluding Edge detection for flaw size assessment, such as an automatededge detection using “Canny” method wherein an automated technique fordetecting the edge of images for a hole is utilized. While the Cannymethod is know for detecting edges in prior art images, it is believedto be novel to not only detect edges but then determine a size of a flawin IR images automatically. Using this method, a flaw might be selectedby selecting a region around the flaw. While the Canny method is knownfor detecting edges, the use of the technique for measuring flaw size isnovel. For example, in FIG. 35, the hole with the line or another holemay be selected by producing a box around the hole. The Canny method canthen be used by the computer to automatically detect the edge of theflaw and the computer then determines the size of the flaw. It will beappreciated that a vertical line through the center of the hole or ahorizontal line through the center of the hole can be graphed in termsof pixel brightness that ranges from dark near the edges to bright nearthe center and then dark again. It will be appreciated that for a holein the drawing a bell shaped curve is produced for pixel intensityversus the x pixel line or y pixel line as the image moves from dark tolight and to dark again. A desired value or percentage change or thelike for the boundary value can be selected and whereupon acircumference can be plotted around the hole. The computer can beprogrammed to measure the circumference for flaw sizing. The entireoperation can be automated by letting the computer detect the desiredamount of change that indicates the boundary. Various types of filtersmay be utilized for this purpose. For example if the range from dark tolight in pixel value is from 10 to 40, then a pixel value ofapproximately 15 might be selected as the boundary or a desiredpercentage change necessary to indicate a boundary of a flaw. Once thedesired amount of pixel change is known, then for other flaws or holes,the entire process of sizing the flaw is automated. Various types offilters such as Gaussian filters and the like may be utilized fordetection of edges, as is known by those of skill in the art now havingthe benefit of this disclosure.

Image gray value profiling along vertical or horizontal lines or usingpeak of values.

A profile along chosen cross hair can be provided. For example, avertical and horizontal line may be utilized to pick a particular pixelin an image. As a non-limiting, cross-hairs could be placed using thecomputer in a flaw or hole in a maximum raw contrast image. In thiscase, with the cross-hair might select x pixel=140 and y pixel=108. Thentwo graphs could be produced. For the vertical profile at x pixel=140the graph may display the Value for maximum raw contrast versus the Ypixel value. For the horizontal profile at Y pixel=108, the graph maydisplay the value for maximum raw contrast versus the x pixel. If ahorizontal line goes through a series of holes the pixel values versusthe pixel position would show a series of peaks and valleys.

Area Statistics. Standard deviation is measure of noise in the data andis used to compute signal-to-noise ratio. For example, an area could beselected such as in a processed normalized contrast frame or anotherframe. This area can be enlarged and the mean and standard deviation iscalculated in the selected area.

Chosen pixel coordinates can be displayed to measure flaw size on any 2Dimage.

Analyze Saved images methods are integrated in Analyze Saved imagescomputer screen panel. The Images method creates a mosaic or multipleimages, e.g. four images, in each file. Image Comparison method comparesimages to reference images and may compare multiple images as desired.The process involves image registration to reference images and thensubtraction from the reference images. The process may selectivelyutilize a mosaic of reference images and a separate mosaic of evaluationimages. The embodiment is operable to show a mosaic of registeredevaluation images and a separate mosaic of superimposed registeredevaluation images with reference images. The embodiment is operable toshow subtraction of registered images from reference images to assessgray value (e.g. peak of normalized contrast) difference in indications.Multiple image files may be opened with an image registration between afirst set of image files and a second set of image files if desired.Storage may be provided for a mosaic of analysis images, a mosaic ofreference images, a mosaic of registered analysis images, a mosaic ofsubtraction of registered analysis images from reference images, and/ora mosaic of superposition images of registered analysis images andreference images. Operations on these images may also include contrastadjustment, length measurement, and saving the images in file formatssuch as .mat, .fig. jpg and the like. Examples might include a mosaic ofreference images such as maximum normalized contrast or a mosaic ofevaluation images such as maximum normalized contrast. Other mosaicsmight include registered evaluation images or superimposed registeredevaluation images with reference images.

Thus, differences in the thermal response (e.g. normalized contrast) canbe quantified and used for monitoring the material condition for flawgrowth.

Flash Thermography Response, Accept/Reject Threshold and POD Analysis

Use of Peak Contrast in Probability of Detection (POD) Analysis

Diameter-to-depth ratio (D/d) can be used to establish a correlationwith peak contrast for POD analysis as peak contrast provides amonotonic change with the diameter-to-depth ratio. As discussed herein,an exponential fit equation may be used and provide a resulting 90%predication bounds and 95% confidence bounds. Depending upon theaccept/reject detection threshold level used for peak contrast, a90/95POD size of the diameter-to-depth ratio can be directly read from theseplots. Such an approach gives a linear relationship between diameter anddepth for 90% POD with 95% confidence. A more complex relationship canbe obtained using peak contrast map method given below. For example, afirst graph may display peak contrast v D/d ratio with 95% boundariesshown in the graph. A fit curve may be provided at roughly thecenterline between the 95% boundary lines. Various points for peakcontrast vs D/d ratio may be plotted onto the graph. Some of the pointsfor peak contrast vs D/d ratio may fall just outside the 95% boundarieswith other points being inside. In another example, the same graph maybe provided with 90% boundaries shown on the graph where all of thepoints for the peak contrast vs D/d ratio all fall within the 90%boundary lines.

Using Calibration Data in Probability of Detection (POD) Analysis

Peak contrast parameter calibration data from FIG. 32 can be used topredict peak contrast C_(peak). Here, it is assumed that thethermography response is modeled as a surface in the calibration data.In another embodiment, the method utilizes a polynomial function surfacefit to the peak contrast data as discussed herein. Interpolated datafrom FIG. 32 can be used for prediction. The predicated peak contrastcan be used as the transformed flaw size parameter denoted as ‘a’ inMIL-HDBK-1823. The diameter-depth pairs are transformed into peakcontrast values using the fitted or interpolated calibration peakcontrast data. The actual peak contrast (Cpeak) is used as the signalresponse denoted as ‘â’. It should be noted that the noise is alsomeasured in peak contrast units. If interpolated calibration data isused, it is recommended to use separate artificial flaw samples for PODanalysis. If fitted data is used, the calibration sample can be used forPOD analysis. The ‘â’ versus the calibration transformed ‘a’ PODanalysis provides the a90/95 peak contrast value. The value needs to beconverted to an a90/95 relationship between the diameter and depth.

Using FIG. 36, the 90/95 diameter-depth pair line can be predicated asan intersection of peak contrast fit map with a plane defined by a90/95peak contrast value. Here, the surface was fitted as a second orderpolynomial or ellipsoid. The intersection line is given as an equationof an ellipse. In an example given in FIG. 36, substitute f(x, y)=0.1which is the chosen value for peak contrast (normalized −1 to 1). Thisprovides an equation defining the 90/95 diameter-to-depth relationship.The equation defines an elliptical arc which is probably a betterdescription of a90/95 than the linear relationship obtained usingdiameter-to-depth ratio POD analysis. This POD approach is expected tosignificantly reduce number of flaws needed in the POD study. Theapproach can be referred to as the calibration transformed POD.

Finally, the calibration peak contrast maps can be directly used in thePOD analysis. For the a90/95 estimate use FIG. 37 which provides 90%prediction bounds of peak contrast surface in Matlab surface fit tool.The 95% confidence bound at a desired diameter-depth pair can becalculated from the fit equation given in FIG. 19. Using FIG. 37, thea90/95 diameter-depth pair line can be determined as an intersection ofthe 90% upper bound peak contrast fit surface with a plane defining peakcontrast detection threshold value similar to that shown in FIG. 36.This approach can be described as POD from predication bounds to thecontrast map.

Flaw size parameter, a90/95 calculated using the normalized contrast isprimarily affected by the detection threshold-to-noise ratio. Therefore,the a90/95 diameter-depth relationship is transferable betweenthermography set-ups, if detection threshold-to-noise ratio iscomparable to that in the POD study.

Using Simple Contrast and Referenced Simple Contrast in Flaw Detection

A reference flaw specimen or a reference test specimen can beaccommodated with the test object or used concurrently, both appearingin the camera field of view at the time of the data acquisition. Areference test specimen may not have a programmed flaw in it and isanomaly free with same material specification as the test object. Areference flaw specimen has at least one programmed flaw in otherwiseanomaly free material with same material specification as the testobject.

The reference flaw specimen, used concurrently, can be used to assist insimple contrast evaluations. Concurrent use of reference helps in allcontrast methods where monitoring of contrast based parameters isdesired.

The relative pixel intensity or the simple contrast is given by,R=W−W _(ref),  (43)W=pixel intensity at the measurement ROI at post-flash time t,W_(ref)=pixel intensity at the reference ROI at the post-flash time t,and

Since simple contrast is dependent upon the flash intensity and cameratype among other factors, simple contrast from a reference flaw can beused to normalize the simple contrast.

The simple contrast of a reference flaw is given by,R _(f) =W _(f) −W _(ref),  (44)where,R_(f)=simple contrast of a reference flaw andW_(f)=pixel intensity of ROI at a reference flaw.

Peak contrast from a reference flaw can be used to calculate referencedsimple contrast as,R _(ref) =R/R _(f,peak),  (45)where,R_(ref)=referenced simple contrast andR_(f,peak)=simple contrast of a reference flaw.

The referenced simple contrast is less dependent on the flashthermography set-up. Similarly, the detection threshold can bereferenced to peak contrast from the reference flaw.

The percent detection (decision) threshold is given as,%R _(thr)=100(R _(thr) /R _(f,peak)),  (46)R_(thr)=detection threshold for simple contrast and%R_(thr)=percent detection threshold.

In other words the referenced simple contrast process may comprise stepssuch as placing the reference flaw standard on or next to the testobject to appear in the IR image concurrently with the test object.Subsequently, the flash IR data acquisition is performed. The referenceROI in on the reference standard good area. The relative pix intensityonthe test object is calculated using equation 43. The simple contrast ofthe reference flaw is calculated using equation 44. The referencedsimple contrast is calculated using equation 45. The threshold detectionthreshold is chosen using equation 46.

Using Simple Contrast and Referenced Simple Contrast in Pod Analysis

Since normalized peak contrast provides good correlation to flawcharacteristics (diameter, depth etc.), it is expected that referencedsimple contrast and simple contrast also provide correlation to flawcharacteristics. The methods given here are equally applicable toanalysis using the simple contrast. Therefore, the POD analysis can bedone using the simple contrast (R or Rref) too. Data of the peak simplecontrast (Rpeak) versus the diameter-to-depth ratio can be used in âversus a POD analysis. The referenced contrast calculation needs acalibration standard in an identical test set-up or the referencestandard is used concurrently with the part. This may not be practicalin some cases.

Alternatively, the simple peak contrast map with respect todiameter-depth data can be analyzed to obtain the POD estimates similarto that used for the normalized peak contrast e.g. POD using thecalibration transform and POD using the prediction bound to the contrastmap methods.

The simple contrast is influenced by flash intensity and type of thecamera. Therefore, to maintain detection sensitivity, the detectionthreshold level should be established as a percentage of simple peakcontrast (Rf) from a selected reference calibration flaw. Similarly peakcontrast can be normalized to the reference flaw peak contrast by simplydividing by the reference flaw peak contrast. The resulting contrast iscalled the referenced simple contrast (Rref,peak). The influence ofcamera model and flash intensity are minimized if same value of thepercent detection threshold is used between set-ups.

The a90/95 flaw size is primarily related to the detectionthreshold-to-noise ratio and percent threshold level. Therefore, thea90/95 diameter-depth relationship obtained using the simple contrast(Rpeak or Rref,peak) is transferable between thermography set-ups if thedetection threshold-to-noise ratio and percent detection level arecomparable to that used in the POD study. The simple contrast has lessnoise compared to the normalized contrast and may provide a smallera90/95 flaw size for a given threshold-to-noise ratio.

A pass/fail POD per MIL-HDBK-1823 is also possible. Here, a thermographyframe with most visible contrast is selected from appropriatelyprocessed data sequence. A consistent flaw detection procedure isapplied in the POD study. The result is noted as “pass” if the flaw isdetected and “fail” if the flaw is not detected. The flaw diameter-depthpairs are transformed to diameter-to-depth ratios (D/d). The resultingdata is used in the POD analysis. Alternatively, the diameter-depthpairs are transformed to the calibrated normalized or calibratedreferenced simple peak contrast. This data is then used in the pass/failPOD analysis. This approach is likely to provide even smaller a90/95diameter-to-depth values compared to the â versus a approaches as theflaw detection is based on perception of the flaw by viewing images.

CONCLUSIONS Normalized Contrast and Derivative (NCD) Method

The embodiments described herein are applicable to flash (pulsed)thermography nondestructive evaluation. They are an enhancement of thecontrast and feature imaging methods previously described in U.S. Pat.Nos. 9,066,028 and 8,577,120. Two different methods of the derivativeshave been provided. The first method involves derivative of the raw(method 1A) contrast evolution data or the smooth (method 1B) contrastevolution data using the smoothing during the calculation ofderivatives. The second method involves derivative of the curve fit(method 2) contrast evolution which may be fitted to either the raw orsmooth data. In method 2A, curve fit is also a simulation fit (e.g.polynomial fit) to the contrast simulation. A variation of the secondmethod i.e. method 2B involves curve fit (non-simulation) to the raw orsmooth contrast evolution and then the derivatives are computed from thecurve fit equation.

Both methods convert acquired flash thermography IR data to contrastvideo sequence data with or without smoothing. Subsequently, bothmethods can be used to convert contrast video sequence data to firstderivative video sequence data and the second derivative video sequencedata.

Then, several non-derivative (e.g. contrast) and derivative (e.g. firstand second derivative) features are extracted as well as features atselected frame numbers are extracted as images. Depending upon feature,the images may reveal anomaly information such as the anomaly depth(e.g. frame number related images), anomaly size (e.g. contrast andcontrast derivatives), anomaly gap thickness (e.g. peak product time)and provide suppression of temporal and spatial noise. All images aredescribed as the contrast A-scan, contrast B-scan, contrast value C-scanand contrast time C-scan similar to the traditional ultrasonicpulse/echo scans.

There are advantages as well as disadvantages of each method. Thesimulation fit method 2A and the non-simulation curve fit method 2Bmethods require that the curve fit is established first. If the fitscannot be established accurately due to non-ideal test conditions, lowcontrast value, choice of fit model etc., then the results would be inerror. However, the derivatives are calculated from the fit equationsand therefore the resolution is excellent compared to that frommethod 1. The smooth derivative values of method 1 depend upon thelength of the smoothing span. Longer smoothing span provides bettersuppression of the pixel temporal noise but they also flatten thecontrast evolution. The smoothing in derivatives also shifts the peak inthe first derivative which may be used to your advantage in manysituations. Detection of flat bottom holes in the provided imagesvalidates the method of contrast and feature imaging.

Converted Contrast and Derivative Method (CCD)

This method is also applicable to flash (pulsed) thermographynondestructive evaluation, for it is a variation of the contrast andfeature imaging method previously described in U.S. Pat. Nos. 9,066,028and 8,577,120. Here, a contrast method of implied reference (ROI) isintroduced. The method allows extraction of the pixel intensity basedconverted evolutions that have similar shape characteristics to theshape of the corresponding normalized contrast evolution. Fordelamination like void, the shape of the normalized contrast evolutionis similar to a backward skewed statistical distribution. Thus, theconverted contrast evolutions can be analyzed by measuring the peakamplitude and peak time. In addition, the first and second derivativescan also be computed. Similar to the normalized contrast A-scan, B-scanand C-scans, the converted contrast also provides A-scan, B-scan andC-scans. The method uses a multiplier function that is empiricallyderived (or statistically fitted) to convert the relative pixelintensity to the converted contrast. A simple multiplier function usesfractional power of the frame time. More complex but better fittingfunctions can be derived based on the equation fitted to the relativepixel intensity of the reference region. A method is provided to computethe normalized contrast from the converted contrast.

Although, the converted contrast cannot be used for the simulationmatch, the converted contrast method provides an advantage over thenormalized contrast by eliminating or reducing use of the reference ROI.For flaw detection, results from the converted contrast are as good asor better than the normalized contrast due to use of larger numberswhich provide better graphical display in the software used. Dependingupon the choice of the multiplier function, the converted contrast mayshow the afterglow/texture effect in the images. However, it may headvantageous to have the ability to evaluate data for texture effects.Filtering or gating the evolution can be used to reduce theafterglow/texture effect if desired as well as frame gating is used toexclude earlier images from analysis.

Normalized Temperature Contrast and Derivative (TCD) Method

This method computes surface temperature video sequence data. Normalizedtemperature contrast and smooth normalized temperature contrast videosequence data are computed. Temperature rise video sequence data andsimple contrast video sequence data are computed in this method. Firstand second derivative video sequence data of smooth normalizedtemperature contrast data are also computed. Selected Frame ImageAnalysis (FIA) and Extracted Image Analysis (EIA) are also applicable toNormalized Temperature Contrast method. Similar to that for thenormalized contrast video, A-scan, B-scan and C-scans are possible withthe normalized temperature contrast and the imaging results arecomparable or better than the normalized contrast A-scan, B-scan andC-scans due to correction for the varying reflection temperature.

The temperature contrast method reduces influence of diffused reflectionfrom the part surface, enhancing the contrast. Also, surface temperaturemeasurements are more quantitative than the pixel intensity measurementswhich contain both the emissive and reflective components of irradianceforming the image.

Contrast Evolution Calibration and Analysis (CECA) Method

This method provides an empirical method of calibrating the flashthermography response in nondestructive evaluation. The contrastcalibration method (CECA) is applied to the normalized contrast data fora pixel with peak of relative contrast for the indication.

First a physical calibration standard with artificial flaws such as flatbottom holes with desired diameter and depth values in a desiredmaterial is fabricated. Long flat bottom slots can be used incalibration standard. For tight delaminations, use a standard thatsimulates the desired condition. U.S. Pat. No. 8,577,120 provides acomparison of normalized contrast response from slots and holes. It mapsslot width to equivalent flat bottom hole width, which then can be usedin evaluating depth of long indications.

Normalized contrast evolution data for each artificial flaw in thereference standard is preferably extracted from the raw video sequencedata. The contrast evolution files are analyzed in the contrastevolution evaluation methods provided. Six contrast parameters areextracted for each flaw in the calibration standard. A calibration dataset is prepared from the contrast parameter data. The calibration datais plotted by using an evolution evaluation and calibration methoddescribed here. In order to analyze a given contrast evolution for flawdepth, contrast evolution parameters are calculated, diameter or widthsare measured in 2D images of the anomaly. Depth is preferably assessedin the method by using the anomaly diameter or width, the six contrastevolution parameters and calibration data. A single depth estimate canbe interpolated from the multiple depth estimates, one each from the sixevolution parameters.

Other Image and Data Analysis Methods

These methods provide Frame Images, Extracted Images, and Analyze savedimages, which may be selectively chosen for activation from a computerscreen. Video frame images are called Frame Images here. A frame numberis associated with the frame image and may be referred to herein as aframe number wherein the frame number calls for a frame image. Imagesextracted by scanning for values from multiple frames are calledExtracted Images. A single frame number is not associated with extractedimage.

For each of the three video sequence data types (e.g. normalizedcontrast, converted contrast and normalized temperature contrast), thereare other sub-methods including, flaw size measurement, edge detection,image gray value profiling along vertical or horizontal lines and imagegray value profiling using peak values scanned along pixel lines invertical and horizontal directions. In accord with the embodimentsdescribed herein, the methods to create these video sequence data types,which may also be referred to as video data herein, may be pulled upfrom one or more computer screens, some of which were discussed or shownherein. The computer screens provide the options for each of the stepsduring process.

Under analyze saved images a sub-method of image comparison(registration, subtraction and superimposition) to assess changes inthermography response (i.e. raw or processed pixel intensity data) andimage tiling or mosaic is also used. They provide useful informationdepending upon user need. Accordingly, the embodiments described hereinprovide display of computer imagery that allow selection of imagecomparison methods.

Saved images are further analyzed by creating a mosaic. The images canbe compared to reference images by a process called image registrationand then subtracted from the reference images. Thus, differences in thethermal response (e.g. normalized contrast) between pairs of images canbe quantified.

Thermography Response, Accept/Reject Threshold and Pod Analysis

The methods given here provide quantitative thermography response suchas the peak normalized contrast, peak converted contrast and peaknormalized temperature contrast, simple contrast and referenced simplecontrast etc. that can be used for flaw detection based on establishingaccept/reject threshold level for thermography response. Thesethermography responses can be used in probability of detection (POD)analysis based on thermography response correlation to diameter/depthratio (Method 1A).

The diameter/depth ratio curve fit to thermography response can be usedas transformation of diameter/depth ratio to predicted thermographyresponse (e.g. peak contrast). Using linear correlation between actualthermography response and predicted thermography response along withdecision threshold for actual thermography response, a90/95 forpredicated thermography response can be calculated. The a90/95predicated thermography response is then transformed back to a90/95 fordiameter/depth ratio (Method 1B)

Similarly, correlation of thermography response to both diameter anddepth given as fitted surfaces and thermography response threshold planeis used perform POD analysis (Method 2A).

Another approach uses the calibration data for thermography response mapas a transformation matrix to convert the diameter-depth pairs topredicted thermography response which is used as the transformed flawsize parameter to correlate to actual thermography response. Usinglinear correlation between actual thermography response and predictedthermography response along with decision threshold for actualthermography response. a90/95 for predicated thermography response canbe calculated. The a90/95 predicated thermography response is thentransformed back to a90/95 for diameter/depth ratio (Method 2B). Acouple of pass/fail POD approaches are also provided (Method 3).

The foregoing description of the exemplary embodiments has beenpresented for purposes of illustration and description only. It is notintended to be exhaustive, nor to limit the exemplary embodiments to theprecise form disclosed; and many modifications and variations arepossible in light of the above teachings. Such modifications andvariations that may be apparent to a person skilled in the art areintended to be included within the scope of the inventive concepts asdefined by the accompanying claims.

The invention claimed is:
 1. A method to create video data for infraredflash thermography, comprising: selecting a frame of a video comprisinga sequence of frames of surface temperature in terms of pixel intensityof a surface of a material under evaluation after application of a flashof heat; selecting a measurement region of interest (ROI) and areference ROI in said frame; and computing video data corresponding withthe following equations for a plurality of frames after said flash ofheat:C ^(C) =ΔWf where, C^(C)=a converted contrast, ΔW=a change in a pixelintensity after said flash of heat, and${f(t)} = {\frac{B}{\Delta\; W_{ref}} = \frac{{Bt}^{- s}}{M}}$ where,B=a baseline value, ΔW_(ref)=change in said pixel intensity of saidreference ROI after said flash of heat, and M=a multiplier that dependson a highest pixel intensity value that occurs after said flash of heat.2. The method of claim 1, further comprising utilizing raw video orsmoothed video for said step of computing.
 3. The method of claim 2,further comprising computing a slope and a multiplier consistent withthe following equations: $\begin{matrix}{{s = {\frac{d\;{\ln\left( {\Delta\; W_{ref}} \right)}}{d\;{\ln(t)}} = {\frac{d\left( {\Delta\; W_{ref}} \right)}{d\; t}\frac{t}{\Delta\; W_{ref}}}}},{and}} & \; \\{{M = {e^{({{l\;{n{({\Delta\; W_{ref}})}}} - {s\; l\;{n{(t)}}}})} = \frac{\Delta\; W_{ref}}{t^{s}}}},} & \;\end{matrix}$ where, s=a slope of a selected portion of a log-logreference relative pixel intensity plot, M=a multiplier.
 4. The methodof claim 2, further comprising selectively computing said convertedcontrast C^(C) from a normalized contrast C^(W) or selectively computingsaid normalized contrast C^(W) from said converted contrast C^(C), where$C^{W} = {{{\frac{{\Delta\; W} - {\Delta\; W_{ref}}}{{\Delta\; W} + {\Delta\; W_{ref}}}.\Delta}\; W} = {W - W^{0}}}$Δ W_(ref) = W_(ref) − W_(ref)⁰ where, C^(W)=said normalized contrast ata time t, ΔW=change in a pixel intensity of said measurement ROI aftersaid flash of heat, W=said pixel intensity at said measurement ROI at apost-flash time t, W⁰=said pixel intensity at said measurement ROIbefore said flash of heat, ΔW_(ref)=change in said pixel intensity ofsaid reference ROI after said flash of heat, W_(ref)=said pixelintensity at said reference ROI at said post-flash time t, and W_(ref)⁰=said pixel intensity at said reference ROI before said flash of heat.5. The method of claim 4, wherein said converted contrast C^(C) isrelated to normalized contrast C^(W) consistent with the followingequation: $\begin{matrix}{{C^{W} = {\frac{C^{c} - C_{ref}^{c}}{C^{c} + C_{ref}^{c}} \cong \frac{C^{c} - B}{C^{c} + B}}},} \\{C^{c} \cong {B\;{\frac{1 + C^{W}}{1 - C^{W}}.}}}\end{matrix}$
 6. The method of claim 5, further comprising creating forsaid plurality of frames additional video data consistent with thefollowing time derivative: a first derivative C^(C)′=dC^(C)/dt.
 7. Themethod of claim 6, further comprising creating for said plurality offrames additional video data corresponding with the following timederivative: a second derivative C^(C)″=dC^(C)′/dt.
 8. The method ofclaim 6, wherein said first derivative is taken when said C^(C) iscomputed utilizing said raw video and then smoothing during saidcreating of said first derivative.
 9. The method of claim 6, whereinsaid first derivative is taken when said C^(C) is computed utilizingsaid smoothed video produced from said raw video.
 10. The method ofclaim 6, wherein said first derivative is taken of a non-simulationcurve fit to said C^(C) for said plurality of frames and wherein saidC^(C) is computed utilizing either said raw video or said smoothedvideo, said first derivative being computed from one or more equationsof said non-simulation curve fit.
 11. The method of claim 6, whereinsaid first derivative is taken of a simulation curve fit to said C^(C)for said plurality of frames and wherein said C^(C) is computedutilizing either said raw video or said smoothed video, said firstderivative being computed from one or more equations of said simulationcurve fit.
 12. The method of claim 7, further comprising combining imagedata from said plurality of frames of said video for at least one ofsaid C^(C) or said C^(C)′or said C^(C)″ to produce an extracted image.13. The method of claim 7, further comprising utilizing image data froma selected frame number of said plurality of frames of said video forsaid C^(C) or said C^(C)′ or said C^(C)″ to produce a frame image. 14.The method of claim 7, further comprising combining image data from aframe number associated with at least one of peak or a minimum or anaverage or a selected frame feature value or a combination of featureimages for at least one of said C^(C) or said C^(C)′ or said C^(C)″ toproduce an extracted image.
 15. The method of claim 7, furthercomprising combining image data from frame numbers associated withvalues above a threshold value for at least one of said C^(C) or saidC^(C)′ or said C^(C)″ to produce an extracted image.
 16. The method ofclaim 7, further comprising combining image data from frame numbers ofat least one of said C^(C) or said C^(C)′ or said C^(C)″ that areassociated with values from a selected vertical or horizontal pixel lineto produce an extracted image.
 17. The method of claim 7, furthercomprising combining image data from frame numbers for at least one ofsaid C^(C) or said C^(C)′ or said C^(C)″ that are associated with valuesfrom a selected pixel to produce an extracted image.
 18. The method ofclaim 1, further comprising measuring a size of a flaw on said frame ofsaid video utilizing a superimposed line and providing a computerizeddistance measurement of said superimposed line.
 19. The method of claim1, further comprising measuring a size of a flaw on said frame of saidvideo utilizing automated edge detection.
 20. The method of claim 1,further comprising monitoring growth of a flaw by producing referenceimages and comparing said reference images to subsequently producedimages.
 21. The method of claim 20, wherein said step of comparing ismade by at least one of image registration, superimposition, orsubtraction.
 22. The method claim 1, further comprising: providing acalibration standard comprising at least one opening having a knowndiameter and a known depth; wherein said step of computing video datafurther comprises utilizing said calibration standard and an anomalyindication as said material under evaluation.
 23. The method of claim22, wherein said reference ROI is selected on an anomaly free area ofsaid calibration standard.
 24. The method of claim 1, further comprisingutilizing a value consistent with a diameter to depth ratio of a flaw asa flaw size input to calculate a probability of detection of a flaw. 25.The method of claim 1, further comprising determining a percentdetection threshold for a probability of detection of a flaw consistentwith the following equationsR=W−W _(ref), where, R=a relative pixel intensity W=a pixel intensity atsaid measurement ROI at a post-flash time t, W_(ref)=a pixel intensityat said reference ROI at said post-flash time t,R _(f) =W _(f) −W _(ref), where, R_(f)=a contrast of a reference flawand W_(f)=a pixel intensity of ROI at a reference flaw;R _(ref) =R/R _(f,peak), where, R_(ref)=a referenced contrast,R_(f,peak)=a contrast of a reference flaw; and%R _(thr)=100(R _(thr) /R _(f,peak)), where, R_(thr)=a detectionthreshold for simple contrast and %R_(thr)=said percent detectionthreshold.
 26. The method of claim 1, further comprising utilizing athermography response to diameter and depth correlation to determine aprobability of detection of a flaw.
 27. A method for characterizing anunknown anomaly in a material, said method comprising: recording flashthermography video data as a sequence of digital frames based onartificial anomalies of known diameter and depth values; computingcontrast evolution data for said artificial anomalies of known diameterand depth values to provide known computed contrast evolution data;calculating contrast evolution parameters for said artificial anomaliesof known diameter and depth values to provide known calculated contrastevolution parameters, said known calculated contrast evolutionparameters comprising at least three of: a peak contrast, a peak time,time scale factor, offset time, begin time, and slope; computing anunknown contrast evolution of said unknown anomaly; and using at leastone of said computed contrast evolution data or said known calculatedcontrast evolution parameters with said unknown contrast evolution ofsaid unknown anomaly to characterize diameter and depth of said unknownanomaly.
 28. The method of claim 27, wherein said step of calculatingknown contrast evolution data comprises a single point extraction for around anomaly or multipoint extraction for a linear anomaly.
 29. Themethod of claim 27, filtering said contrast evolution of said unknownanomaly.
 30. The method of claim 27, wherein computing said unknowncontrast evolution comprises: calculating a normalized contrast, whereinsaid calculating of said normalized contrast comprises calculating aratio of: a change in pixel intensity at a measurement region ofinterest minus a change in pixel intensity at a reference region ofinterest compared to a sum of said change in pixel intensity at saidmeasurement region of interest and said change in pixel intensity atsaid reference region of interest.
 31. The method of claim 27, whereincomputing said unknown contrast evolution comprises calculating a simplepixel intensity contrast, wherein said calculating of said simple pixelintensity contrast is determined by finding a difference between a pixelintensity at a measurement region of interest at post-flash time t and apixel intensity at a reference region of interest at said post-flashtime t.
 32. An apparatus for characterizing an unknown anomaly in amaterial comprising: a processor, and a memory storing instructionsthat, when executed, cause said apparatus to: calculate contrastevolution parameters for said unknown anomaly comprising at least threeof: a peak contrast, a peak time, time scale factor, offset time, begintime, and slope; and wherein a determination of a diameter and a depthof said unknown anomaly is performed by relating said calculatedcontrast parameters for said unknown anomaly to contrast parameterscreated from a calibration file empirically derived from a plurality ofartificial anomalies of known diameter and depth.
 33. The apparatus ofclaim 32 wherein said instructions cause said apparatus to produce acontrast evolution and to filter said contrast evolution.
 34. A methodfor characterizing an unknown anomaly of unknown diameter and depth in amaterial, said method comprising: recording flash thermography videodata as a sequence of digital frames based on artificial anomalies ofknown diameter and depth values; recording flash thermography video dataas a sequence of digital frames based on said unknown anomaly of unknowndiameter and depth; calculating contrast evolution parameters in acalibration file comprising at least three of: a peak contrast, a peaktime, time scale factor, offset time, begin time, and slope from saidartificial anomalies of known diameter and depth values; and determiningsaid unknown diameter and depth of said unknown anomaly utilizing saidcalibration file.
 35. The method of claim 34 comprising producing atleast one of a normalized contrast, converted contrast or a normalizedtemperature contrast.